{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sound Generation with AudioLDM2 and OpenVINO™\n",
    "\n",
    "[AudioLDM 2](https://huggingface.co/cvssp/audioldm2) is a latent text-to-audio diffusion model capable of generating realistic audio samples given any text input.\n",
    "\n",
    "AudioLDM 2 was proposed in the paper [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://arxiv.org/abs/2308.05734) by `Haohe Liu` et al.\n",
    "\n",
    "The model takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional sound effects, human speech and music.\n",
    "\n",
    "![](https://github.com/openvinotoolkit/openvino_notebooks/assets/76463150/c93a0f86-d9cf-4bd1-93b9-e27532170d75)\n",
    "\n",
    "In this tutorial we will try out the pipeline, convert the models backing it one by one and will run an interactive app with Gradio!\n",
    "\n",
    "\n",
    "#### Table of contents:\n",
    "\n",
    "- [Prerequisites](#Prerequisites)\n",
    "- [Instantiating Generation Pipeline](#Instantiating-Generation-Pipeline)\n",
    "- [Convert models to OpenVINO Intermediate representation (IR) format](#Convert-models-to-OpenVINO-Intermediate-representation-(IR)-format)\n",
    "    - [CLAP Text Encoder Conversion](#CLAP-Text-Encoder-Conversion)\n",
    "    - [T5 Text Encoder Conversion](#T5-Text-Encoder-Conversion)\n",
    "    - [Projection model conversion](#Projection-model-conversion)\n",
    "    - [GPT-2 conversion](#GPT-2-conversion)\n",
    "    - [Vocoder conversion](#Vocoder-conversion)\n",
    "    - [UNet conversion](#UNet-conversion)\n",
    "    - [VAE Decoder conversion](#VAE-Decoder-conversion)\n",
    "- [Select inference device for AudioLDM2 pipeline](#Select-inference-device-for-AudioLDM2-pipeline)\n",
    "- [Adapt OpenVINO models to the original pipeline](#Adapt-OpenVINO-models-to-the-original-pipeline)\n",
    "- [Try out the converted pipeline](#Try-out-the-converted-pipeline)\n",
    "\n",
    "\n",
    "### Installation Instructions\n",
    "\n",
    "This is a self-contained example that relies solely on its own code.\n",
    "\n",
    "We recommend  running the notebook in a virtual environment. You only need a Jupyter server to start.\n",
    "For details, please refer to [Installation Guide](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/README.md#-installation-guide).\n",
    "\n",
    "<img referrerpolicy=\"no-referrer-when-downgrade\" src=\"https://static.scarf.sh/a.png?x-pxid=5b5a4db0-7875-4bfb-bdbd-01698b5b1a77&file=notebooks/sound-generation-audioldm2/sound-generation-audioldm2.ipynb\" />\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "[back to top ⬆️](#Table-of-contents:)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "stable-audio-tools 0.0.19 requires auraloss==0.4.0, which is not installed.\n",
      "stable-audio-tools 0.0.19 requires descript-audio-codec==1.0.0, which is not installed.\n",
      "stable-audio-tools 0.0.19 requires importlib-resources==5.12.0, which is not installed.\n",
      "stable-audio-tools 0.0.19 requires laion-clap==1.1.4, which is not installed.\n",
      "stable-audio-tools 0.0.19 requires local-attention==1.8.6, which is not installed.\n",
      "stable-audio-tools 0.0.19 requires prefigure==0.0.9, which is not installed.\n",
      "stable-audio-tools 0.0.19 requires v-diffusion-pytorch==0.0.2, which is not installed.\n",
      "stable-audio-tools 0.0.19 requires webdataset==0.2.100, which is not installed.\n",
      "stable-audio-tools 0.0.19 requires ema-pytorch==0.2.3, but you have ema-pytorch 0.7.7 which is incompatible.\n",
      "stable-audio-tools 0.0.19 requires k-diffusion==0.1.1, but you have k-diffusion 0.1.1.post1 which is incompatible.\n",
      "stable-audio-tools 0.0.19 requires pandas==2.0.2, but you have pandas 2.2.3 which is incompatible.\n",
      "stable-audio-tools 0.0.19 requires pytorch_lightning==2.1.0, but you have pytorch-lightning 2.5.1.post0 which is incompatible.\n",
      "stable-audio-tools 0.0.19 requires PyWavelets==1.4.1, but you have pywavelets 1.8.0 which is incompatible.\n",
      "stable-audio-tools 0.0.19 requires sentencepiece==0.1.99, but you have sentencepiece 0.2.0 which is incompatible.\n",
      "stable-audio-tools 0.0.19 requires torchmetrics==0.11.4, but you have torchmetrics 1.7.1 which is incompatible.\n",
      "stable-audio-tools 0.0.19 requires vector-quantize-pytorch==1.14.41, but you have vector-quantize-pytorch 1.22.16 which is incompatible.\n",
      "stable-audio-tools 0.0.19 requires wandb==0.15.4, but you have wandb 0.19.11 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "import platform\n",
    "\n",
    "%pip install -qU accelerate \"diffusers>=0.30.0\" \"transformers>=4.43,<4.52\" \"torch>=2.1\" \"gradio>=4.19\" \"peft==0.17.1\" --extra-index-url https://download.pytorch.org/whl/cpu\n",
    "%pip install -q \"openvino>=2024.0.0\"\n",
    "\n",
    "if platform.system() == \"Darwin\" and platform.processor() != \"arm\":\n",
    "    %pip install \"numpy<2.0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from pathlib import Path\n",
    "\n",
    "if not Path(\"notebook_utils.py\").exists():\n",
    "    r = requests.get(\n",
    "        url=\"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py\",\n",
    "    )\n",
    "    open(\"notebook_utils.py\", \"w\").write(r.text)\n",
    "\n",
    "# Read more about telemetry collection at https://github.com/openvinotoolkit/openvino_notebooks?tab=readme-ov-file#-telemetry\n",
    "from notebook_utils import collect_telemetry\n",
    "\n",
    "collect_telemetry(\"sound-generation-audioldm2.ipynb\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiating Generation Pipeline \n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "\n",
    "To work with [AudioLDM 2](https://huggingface.co/cvssp/audioldm2) by [`Centre for Vision, Speech and Signal Processing - University of Surrey`](https://www.surrey.ac.uk/centre-vision-speech-signal-processing), we will use [Hugging Face Diffusers package](https://github.com/huggingface/diffusers). Diffusers package exposes the `AudioLDM2Pipeline` class, simplifying the model instantiation and weights loading. The code below demonstrates how to create a `AudioLDM2Pipeline` and generate a text-conditioned sound sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2bc1e40277514a17abe714fe7c897fe3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading pipeline components...:   0%|          | 0/11 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Expected types for language_model: (<class 'transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel'>,), got <class 'transformers.models.gpt2.modeling_gpt2.GPT2Model'>.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e116efcb2da643e6aa5ab51518c7ddd2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/150 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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8SUArguYAOsJBPVoBT8Afey4Gk/6c++ZGy3ryfAtGavuyADOFWzuFwIVB5v3iwbv/yQAMgFV90QFWv1T+mYQIf7n/QULaPnYAUwDdP9i++D8/gQg9AAA0AZS9ZsGPAg9+IoD8AMQ+RoC0wR4/ZYBvQMq/dr+l/5T/af89v36A7v/IwDKB3/+hACiCEj8mv+ZBl33Ev5yAZz3bAZvAi4AeAfR/hcJOgeuADwKr/wq+1MFS/of/IcC1/q6/RkGmwCAADADoP/V+v79JgXD/hgD8gaM9aD46gKz97z/zAjU95f7cQR5+SwFuQldAjH+S/xi/cn0/gG5Ca7+ygPQAVX00veRA94BEfrpB1MDMuuKEigIZuwjHZcAVOOUFCP5Ieo4GyIEwPlEDOH+3vsF+74S+Qao7BAOgPow3l4bDAyw4e0jNg4x0UwgcRLwz4gnTBDuyHcivQqF5MMlvwwd9ir0owWFC13g2CBJEmLC+xZ3A3rMljGfFonbzRr8Ba3hGg/cG9f6vPJSEmj3sOVgLQcRMeYqIvT4Btk2HF4G9Oh/Cg362t3L/70K2PIGAJ//A+4+89UKXQz2+VIJg/Uk4a4AUvxF+7QGAvQQ8ljtSvRoAkn8RgTO/GPz/PqfBGcEmAIbCZfxk++lA5fzzwHfC2L1IPys/pT2xgLpCJUETwOl+Kz3XPUq/d8MpQLu/lD4Su1a9eMDzQghBzoGivta8lcFbwirB0gUhvwj9Mn80vuYDycW7Ajk/9L26vhtDvEQdQ07BZPwDPOK/1MPAxDfB8kAF+wK+Q4JPQq+Fi0Dff679pj0kBxEA74AvhlQ4UT47h3L8FUL6xN95ksCgwmG+YYOy/v7/uv9D+xiE1f1Yu6jIcfnmPRkIv7h1fuGGK3ocv3NFc7zUfvOD5f7T/wFDFP+sPSn/y4AWPTCBgMMz/MFCWUHS/asDbj9Y/p1AsfzJglTBp79SQyL/LD3eQepCIH/EwEIDnvw/vocGorx6AXoE5brMgHKB6D5QwNRAxIJE/Qn+/AY2egw/AEjKNR9Akkf6tbhDoAacNykAiwVg+4A/nAcd/TP7hgXJfRL6/cOFgDs7fr9kxQa7P3voyZN5VPp6i3O463sBTO05V3h2C6Y5cjg9DXz7I7gszBQ95HlSyBU/4DV9QX/Gajl1AaoITrX7uwoIXzrXPQkIpjkXuDaHp/6kO7vH6nyU9+QGYYGgPiYEYXz0OVV+wIHQAy4+nD7XvrQ4doLpRHp580Fivsn4s0MjBK9+i4J1AiL9UH/jgry/oH2OAIFByX4KQ+zFYfwQQr2CdDukg6qBjfv8v3U/6770AEaDDr63fLdB2z+8PoEEBoCCvQ2DxcHDfVoD4r+GPCBBG8F+f7IAj4O6fXg8osXx/2Q/McZRPMs768QfQFE+UAQ7QPo6q8Hnwqv8REHrgsv9XsAMhV6BGT9HBUzAQjv2hALBX7uCw6QAIvqTQZvBbXwmv3UBIz4YQPTEp4Aje0rBB/9ePN3GvQGrexFCr0ASPlNEqcJdfb9/jYIcQFmBToJm/ZT8VoFvgUn/2YL8P+c7AUFQAaw9DcLO/9f7a8GxAaJAUEJJAQl+a/5NgYFAKz5Pv/x9Gv6/wbXA8YB2/h0+gj7DAGnBU71sPRF9372WAITAWD6p/bB9k8AZ/ysA7sBpvWVAeT87f0FAr/0kPm5+0b3SQVp/uH4cPyg9PIB8P6u+rMIlfGI+/cPcfX8CBIKVvH6CCYIDP6jCMsCpPsgAZAGOwLcA5oFnQMrAW8Bpgtu+1j5mwni88j5pw/b9sn3hwut9+P+BA3Y/6L//QaJCJ3+8AObCfv2vQHRENL42gFQD9v0tv+wB272APm+C9AFmvlSFsIEQPPQG9cDGvLdEX32t+zHCAT9MfduAFALL/p/+8MQm/GW9MkTjfRC+XgTUvFe9/0J0vW3/KUGJAOb+iYA0wgU9pgDGQ5z9j4DOg1u/AAAaAj195/2LAmSACn+1glfA7P5HQa8Br712wBgAODzAwPmBMEAVv0IBF0Bk/Y3DocA1vQTEWv+cfojDR362fqsAwcCs/5p/aMEV/rj+icPyP+j/O4HX/T+73cFHP0h99oITgGr8sAHaQOC77gHy/xm8N0DGvq3/BkAqvvyBQ33HQNLCK7rpQSZADDxGRJz/6b1EAWL+Of/3gAa/SL+0fWg/30EHvdCABcDifcbCQkJa/vvAy4DSv4OArAFcv7V/IYJagGz+X8KagFB/XEN4/9O90cFYAOV+MYD9gFJ9PQGNwch+YAElAHaAF8EKAX3AYr4dwWYBG354gSl/p31zASaAdf44v/cAav8OgIYBHz52vyHBBMCi/8+BiED1/2fCNoDbfl8Ac8BFfzNAJMCGvr8/YUKd/6M/30JfPvs/I4Dr/bX+Nf/Kv+u/Vr/VAIJ+1ID1gkR/PEA7gXI++n+RgR++nT4owfGAoj+gA0aArn85wiG/wT7wAJCA2sBmwJNBpb8XfvABXT90vyLBEv9YPxdBGUBZP7NAvsDmf1T/+IE9fpG/kEFjPVP+k7+Ifc8AAsBBv4uAPP8VAEs+hT5E/3R9Lz/mf/k+D8A0fwY/DwBIv+J/zb/RQH6AHj+UQJE/r76lv8P+pf0PADG+4P24gTx+HryYQWO+ib27QUv+VX3eQIW/X31uPpOBUj4X/1GDOLyUPulDwTzvAStCrnzFwjDAfX6LQMd/UkGj/4uBNMMh/byCU0IAfdnEPECj/uQDk//I/5cBQEBuf/+A3MJ+/zTAbYI8vtCBSkNggITCEcORQSsAekJdgYJ/rcKpAmu9w8IDg/o9BMI0BG+8dIFMgwH8LL/5gfm+XT/gwbeAw73Awm3DlP0xw5+EPfy6QyqC07xyAAsBxT0+/vAD9j33fmUEI72oPo/Enz+BgABDlv+PvSy/jP+/fAzAewGffHSAuEI+fNDAhwIEfQF+rkGo/XU81IHFvm78TsLA/nF8d8IoPdI+dECEvlf/Ab2pv4b//v1xBDm/r34gg5y8/H5cgPe9c798vnb+6L9rvkgA078nfnNAJj70vpeAoUACPvnAGD9cvYbAO79nvYwAAn9k/ovBI/+G/oG/Mz7sfq6/VwBmvz5/XUBJPnQ/asBp/r+AckBpP2QAO0FqAQG/TcJCv8L8lIN7/4M92kUhv2n92QPXPyN+UcF/v4y/AoCoguN/3QA6Q3l+JH7zw0Z+McCkhHS+tYG2gue/xkGGQ6ZArv7NxCSAXb0/BSyAc7wHRJ2A6ryzAY2CYj2w/nMDgYCwvYNDnICoegTAowHKfCnB8cR1/Qd/osKY/hL/OQNpvx49iEGlv/C+5kKvgb3/BgEswUO/S8BAQt+/6/9YAlx/Mn8ownrAtb+VAJ1/03/XwBEBV0HrwAMBnYGnQAQAKYCzACW9+oASQKX9XsC/ALg+6gFdQRRBQACRgG3Bpn6Bf5PA1z1HPy3AYj7uP43AFD/JfwQ/SYD1vzA+zsCLPqo+Wn/Evov+jn9BPzm+kr9MQJ6/mX9vQDq+db5zQB+/bv68/0R/VL6xP36//77ofu+/Rz8Rvst/vkDcQCj/osHFP8L/H0Flv7W+qr/O/5s/av9BwOjAmX9AQUrAxr+gwO0AFj7W/3c/Jf+df9v/pUEpQB//vUCJP7//C0AGP3K/ZP/gP/aA4ICQAFgASUBevtq/bgEuPnf/YsFx/kT/j0EiP6l/a/9MQQn/4/6fAgw+2nzRgS7/vD45wE0BTkCZwGbCQQM+f0B/y8JXPjp9OkKYgOR9vQJqAfq9Zv9lgaR/Sj6BAg1Bxr9mgbHCyACuv9yBt8EGvpnAPIKJf4p+noEf/7d+EcCiAk//0D7/gfcANz4bAj7Cc4DXAe9BxkDtfuz/zAGBP+5/tEEEv9/+08C+QboAf0AYQnIBAv8WwQZBKP7KQJGAoUA0wHqAF4G+wHX/o0GKgO4/T0DFAI2AS3/W/5pAlX6PvtFAzj9uvy2As4DtQDP+ZMCZQGQ7xL7lAEg89j4xwYoAtr3vANCCRH2MvtQBUT7kP1+ARoFAABl9qgIqAda+AIFgwmZ/Sj5igbvB3rt+f8EE0ntHP3LDorrgfWPBRbxyPnTATr6ZPwIAHL8p/VlBKD9iPHEBo8BDvJCB+UD5vEL/fIBpfkJ+QACjf3+90v/iQB+/Yf97fm+AFz9ifTWBWz/sfBnACj+ofMQ/YEBNPz09XgEWQh++TwGGAQU9fj6/vzH+jD8r/6/BDgAPP18AyD/JgKwA7wDHwNV/d0AawPF/2r+AASHAXL7MwQBBE/7WwTNArL/LgREAPEGCwa0BLQLmQftBLkDhwPcBFcBgQXeDN8CwwVHCysDsQKJBiAEZgGTAjcEXASbAa4DwgYuA4wFJwWZBDQFtwQ/ClcF+gDfBsgBof/IAEP9FP7f+739GAH+/UUA1wLRAED/bf9XAO/9cf57A1sC9ALeA2IBQ/5E/Kz98f5P/ksA+wA3APcBvP4GAsP9GvlxAn/+eP9xBOH+1/4SAFr96ABF/Fn9w/0H+gICuAEHADICqP5u+q77ZPy6/7T9IP6D/ZD3HPqk/eH70P6K/eD7xfqd9yT8nPuy/5/8EQOQ/Tf9CP/j/2D9+P7E/1X8WflY+jP+zvV5/SwAyPo6ARAAvfvG/48DB/8C/4sDcvwK++gFh/6b+Y0Gef1ZAJEFbP9d/+n88v2s/cQBMAPW+4kEGAb7+6sGWAG5+Ov/Q/20/nT/gwMnAmf80AFc/u76dAhE/7f9bAUqAJMBMQVBA3H+wf2p/2T+n/3xAsP+T/5TAcL+/gFuBOAACgRGBG0D7AHl/nEAp/ur/8EFkgAWAFQAE/vx+rD+Y/60/S0Bt///+HUAeQB0/owECgCp/Kv76PzZ/4f+PwOT/2EBMgFy+30BiwGS/FAFvwINBN0DggOZBt0BAgNHBg8D1AS9A/ED0AIU/z4FSQQ7BcMHxgSJAdIBeP1OAPsBkAHmAlkBWASC/rgD1f/j+db9AgJ2/+QEgQQ7AEEBAwPMAbf8rgOpAbkBBwmbBwYDxQYQBOIAqgCA/mL//vyK/1QCAABnAmgENQJTA4wDUgS8AWsC5gRI/a7+4v0/+Fn+ev9w/5oBFwPXAc39EQLq/0/9K/1W/mD6X/6iA5kAYwHCAxP9SP8OAcf6BP6n/t4Bz/5L/p3+h/VF+9/+K/sUAo3/RvwR/pr8KfqQ/bIA3vxAAGMCa/ujAFECPvoA/UD/0fsI/YkDIv0f/dgAlfpH/LMAhfwIAEEB6/pDApMArP5YAE4A+PxY+tf8NfmT+YIBIP3b/V4CFvlJ/uT/xvziA+H/mP6g/v79ugC8/FUEhP/3+OMBc/qe/JUFXAAqARYBlPwA+rj4SPt69nT6Lvzj9o/7w/tk+mj9TwBf/3j6agBV/3/7OAU7AcD9EwZsAFj7WgJX/w/3mwI0A573/wIIAlP5fgU6BDv+FwTyAs76bf3K/0j3dv5lBID61AFWA6j6wgDe/B38RAGEAwwJBQfoBJcDCwIbBQ0GCAYTCXYEoQNfAgUAsAMJASQE+wbpAmcIpQpLBBwF5wJLAjYFpAjCByAH6gUnAHwGwAX8BKoH+wG1A80EJAK1BhUFeAR4Bv8EEQSMAtADAQHF/zIBHfwa/9ACLP7fAVUFwf0HBZcGJf6oBo0Fp/98ArQAcvyO+3H+zPq3+3AECAHAAKYHpv+2ADIGMf8E/loBawDx/1oD5gHv/Bn/s/8o/d/+1v7R+5j8OQGZAjH+/gBM/uH2+f2z/5r5iwF6BAz7XAFkAb75GPw3//n5gPhEASj8E/iYAx38nfhcAzn4yfiO//f4Cfz9/+b6xvpU/KT6Cvmt///+PPou//j7W/o+/2r+tv5v/fP9V/6e/Jn/Rfy0+xD+bfvS/zoAG/5JAQoCuwCe/AD/Vf71+x0B+f4M/RABj//E/4oBTf+N//MApQFdAJcF8QPk/cUFlP/s+aIC7/13/LUEQP7C/UoBU/6iAooEDQI4AtYElwLx/ksIKQS+/u4JkQHt/EUHqQANAd8GPgAXBMgFEgRZBdkDyQFY//YBqwJKAQoB5P+6/BH7Pv/2/uH8ev/EAWv9CwDqAnX7P/1WAZz8JvzaApv+Bfx/BvX/of4MCr8BUP26Bq74evaDAx794/+xBj0CFP80AcIB2P1dABADBv1y/pIERv3/AZMEwv7U/rUAEP62+P3/oAA7+d4EqwZx+RMFHQMR9QMDxQJP+ncG5ATG+tsBDgOM+WEALAQV+ZwAmAgtALIFTAkT/VH+KQVR/n0BaQYJAbACbgVDBDkB2P9iBJwATgKHBL77UfyU/fv6SP4zA7wBBf+9AKT9P/cTAHkD2v0oAkIBAP6x/iUBjADC/SMEYf+v+84AE/3W+uYDif9B/zAEFv/0/+X/e/54+yD7r/2U+xz/xgG6/dIAW/8o/r3/bPzO/iP/wv1oA9b+Zv8rBJQAfQKCA98BQgIwAl8CBALc/1//mf6e/4gDSwJaBB4FpAC+ArIDuQLxA3AFKASKAkMD3gIsAW4BO/4o/Br/ifye/Pr/B/yL+gL/jv7Y/aMBaALI/QkAaf5H+uL+fP6m/h4CKQGyAuUEBQPZAj0BRP97/Sb6//7I/rj9GQBn/oL7nf3J/Ab7pfyx+9f+pP0G/QL/nPse+/T9IvsGAPwC1AAEBDgBCQAT/fX9o/zd+20AWf40/ikCjf8yAPEBIv4HAkQA7ANdA5D9cQEQ/rP+2wP+/mz+DQGp/k4BVwTjArUApAJyAv0A0QMmAzsCZQMNAUYCuwBi/5L+Efxg/Br+3gCJA4cCLQINAGP+Xf8k/nT/HwDM/2oBBv+VAIkClP/YAfD///zt//H/hgA5A7EAQwDl/RL/r/2d/acBqwATAckCVAB4AHAFpQZ1B8UGGwXS/rIBPwO2/okDuAb+AFz//QKn+n36agIy/1f/zgeWBOABgwnrBL8C0wPcAaL8O/4FA6z+5/2ZAi7/Yf57BmAAIv+mAPj+uv3i/RoCAAEc/tADR/66+QEA+vwa+kb/sP8B/Xj/9AVQ/db8IwTK/BX8kATH/ub5oQDR/rf6e//pAKz4Hf6D/871Wfar+v3zDPbvAS74lPjcAP37cvhX/4H9xPsV/ywBB/+9+2YC+P1V/WIBMP7o+hD/N/15+4b+XADDAO7/sQGZ+7f99v6b/w0BFv4zARwAPPuGAjz+M/gEAxr/ZwCZBaoGGgRl/nkD3f/t/OQESwOI/eoEqgOsArIDCgRSAvP8ywPDAdMALgaRBJ4BrAbHBboA/wPMBi/+S/7YAYT3JACwBEsDCQakAlQE9QJjBHsHsQI7BWMHgwGUBwUIaAJCBcwFBwPZAQsGvQTZBHkG2QWzA4cAogH8/tf+NAa1A38BbgFd/br69Prf/qcAkQQvCQMEugHD/VX6Tf3T/LL7Mf3m+4r7dfwZ/7f8JvuC/nn6TPwaAGn9nPzj/3T+bf2HApwBv/8oAaMC1PwMAGYD6/1C/7X96vuq/gMA6gDWALb/XAJs/r/9YP5a+3f+qAGGAzwCCQKDAdL78v2tAPj6nfif+rL3GPr4/AP7gvuA+mP8bACrAaMCdgLiAVv8cQGd/kT9xAT5AaAAvQK0AXL/3QLgAJT+ivoM/rj8gfs7/+r+qP1e/JQCef5q+94E9QB7+wYDgwGX+QkBMwD6/lgBXf1zAIH9Xfpq/G39C/2y/+UHZAPf/cYIhgGd+BsDgf9U/AkCHQQrAWf+rQZOBTX+LwUmAQv7GP0P/pv/4gD5BR4Erv9eAfAAyAGdBp0FrQT8BC4ECQRTBxYIpgQBBhwGUAPHAsMDdAMY/h3/2wFR/ZP7QwAO/d3//wQ/AzMEigIoALT/Of3o+dH7Qfz1+i//tf8b/A7/ov9f+H/7Lvxo+Zn+n/4a/uP/X/zMAMgDlAG0BPMC9AF4AFcBAAA1+R78K/pm+Sn9Gvx3/fAAbwGNAbkAS/4L//786P2S/2//LQHc/+kA5f/xAIwA/f96/5P/l/6v/cX8pv1a/lP+NgNsAtIA1QPTA2EC1AQyBUX/Nf+BALT/8AE/AhEAyP6f//4BxALGAY8BbAQWAYH/mAJP/fb6lf7p/i39egHJA70AbgBgAYgB7//e/8EAqfvh+Rv+7gAw/gj9rgM3/u37UQcrBHEAgAcMAKf/3gDi+5wF2gR4/esDpAPo+BkBUAdW/LIC8QZt/Kv/bgGi/GsBRQEF/ukCvwF3/fkCFgNb+LACtwKM+VgE6AWh/ksEmwJz/pgEFQK3ArcFCQLlANADWwK3/8kGGAWS/pQBb/91+CH8XP6h+8j9sP5O/nL/Iv+fASH+Wv3L/QH/5gDo/uQB2gDR/6UDeQICAdMCdQE5ASYBKvwE/CL+QPt2/YwDXPvd/AwD//jw/DMGZv9RADYH3/8P/7kDV/sk/sMFtfcyAAsFz/VnALAGoPisAZQFpPzoAkUFM/sn/Jf81/LH9lT+6/WX/CwBuvpb/Tz/2fj3AP//0/qP/Cn8AvbT+zb8Wvq9+9j88/or/Yr8ofrY/Fn7rfiI+Xz3Vfc/+4r9Of33/xb+Zf0r/7v8jfmM/ST6//2EAAQBqQIEAn/+sPzm+U36D/7PA3sD9QI2A7wA7QHlBWMDQQI9Auf/XgBIA4wCkATvA/EB0gRmBRcGpAYlBsoFpgSCCecEfQHMApsBRwJNBfAGQQewCQkJEAq3CHsGcAStA1j/Lf80BE0EiwfGCqgLZQvhBwcH2wc+BD0HXQn4BE4FbwR//oD65vsr+0UAhwNtAZ3//fwA+g34/gGIAd3+ugM3/3f5HwHO/Uz+uAFhATMAXwE8AYT/X/8k/wv7J/yk/jUBGgQhA7sBuADh/0n/9vyW/7/9fvsmAtj/v/1VAYIAuP4JAT8CMACqABwEhP7L/gX/DvpO/lX+7v0+AagBOgDQ/8oC+QJAAY0CVv0r+m/9A/s4/yT/If6j/ZT7CPs0+336rPsV/M78CgCs/ob+W/7v/dD+2f2O/1z+HP38Asb/cAC2A3AA/v+tAub+dfy8/ND5yfod/fr+Sf/fAQ3+E/3k/kb9lP+ZAI4BggIO/9D+w/7w/Yf9R//q+Jz6Cfrg+jz+JP+HASUBCAJeAsH8c/+3/3/+sAHDBBoChARFAAwBzf92/+n/PQB1A+QBdAP5BMoA0P/kASEAZgD/AQ3/NwMzAYoDWwMnBnMEMgJYA4gAlvyxAYkD5P7QAuf/W/sO/9sAngHLArUDtwBXAPsCHwD+AaMGwQRYBIgE/gBf/5EDZAVdA5sFUwLr/7j/9v/8/Wz+3wE+Ag8CMwSi/jQA/gB1AmIBNALZAXr+Ov76AOL+VAHGA9j/ZgDNAQgDngL3AMn9Pvf9+2z/cP1nAn0Bw/1l/vz/Kv7cAFT+Xv5v/hv/Mv++/Af/7fuD+w0BKf9O/2MARf7A/qL9p/2b/xP+5P6b/IX7Ov4o/vAAiwFk//T9oP+C/mn+jgHRASkBu/7w/l39WPth/PP8af2C/Qr+pwDiAWMD7QNKAyYBOv/l/70AVgBdA9YDaACt/5b+6fvR/soAaQH5AEMA1/zu/HsAuvu9/cf/j/wH/rf/SwAzAMMAbAJFA+4BqQLb/ij7gfzc/Br/IQIjA2oDQAOj/i3+mP7p/xgC7gEgBP4AJv4FAAj9FPrv/kX+jf+BAjAFBAO4AUAFnQEuBNUDowChAaUAvP6oAKEAZgHLA1wGIAmMA+EGHwdcAV4DSv9K+vb8eQC4/hMAJgKY/34C3gS8AfoBjgEL/zgDjgb2BVwFmQN5ApP/7gIeA7MBaADt/qL96vzG/v7+X/u3+gz7mPsn/3v/3P2l/on8WPvf/AH9Gv4E//j8P/sp+XL5Oftm/J3/Cv7t/JP9Uv3z/yUBoQAjANr/pP/s/aj++P/j/KT7Gvt++BH3lvth+gX92P+C/LL+xf7a+V7+FQJ4/tb/PgO4/dr7Efli+jb5ff37ACf+wP90/wf9U//V/vL92QBJABgB1wAVAx4FIwJ3AzAE7gG/BF4FeAFpAIECYwP4A94HmggcBXoGmwRJATcALgDlAj8BIwI4AiT7cPwxABf+nANTBrIBwAREBwUC/P/4/Vr6bf+YACQBSwO7A8H+Fv2cAU7+L//vA0ABtf3nAfICXf+V/50CPP8l/cf+a/15/WIAGwOSA2ID1QFzAdH/uv4LA5oBRAAPBjAAFv/FA3wC4wVPBKoBhgCm/l8AngCzADj/bv83AK0CEgPCAr8CrgHt/5T9twLZ/3b/jwbvAUD92QJ2AZv78/8PAjf8FABUAvb+GgO//9X+wgL9//cAGgH7AEYB/QBOAkcCG/5Q/B38JfuE/z4BOwP/BNEAN//pAwEB5v5AAvr8Wv0Z/hb9iv/U/IT/sQCn/8f/pv4HACb/h/+J/73++ADZAY4BUQAZ/sr8OfvZ+xv+Df+3/7j+l/yc+tT57Px//jP92v5IAJD+u/7z/039ivyj/qn/XABUArICSAF2Ax3/0/xH/Ur3afn+/Er9NwBdAJgARgJ2/8j/GAJd/+7/+gIJAkQExQSIAHQDRAJD/rEB9QKf/nv+Ov+J+5z9lf9t/2YB/f45/6cBa//JAfwGnAKf/2YFSP3Z/psEB/8FAaIE5vra/0wBlfjRAkQD4vmHA/oGcP1lA8oE9vvPBXMF4/zBA48B0fguAbQCF/skBSgFzQFUBL7/uf7Q/5P/6QEEBVgEJQPfBDME0AJYA1P/xP8E/a36pQPUAaL/6ga3/gX7pgHz/DP+qQLt+yD78/9E/UD+qALM/Rj8Uf36/Ob/JgLSAdACOAKnAHMFPwZkAXsDBwFh+17+kvqL+YUBq/5i/zwGqP41+3gAlfv6+AIBYP6s/QkCmvp0+T3+BvtX+h781PmQ+Db9X/2j/Dv/mft5/YD/8/lY/YX/QPtn/Rb/Gf/9/p8A2QEBAJ3+q/3E/U/9jPvS/UL+y/w5AB4F5QTWBN0Epv6N/fD9vv6wAJkBXv2Q/AQBJ/53//cAXvvu++cCpAIEAuwDDACI/R4BAv8t/VgBOQHAAbECVgI2A9sC1AMeBrwBAv83ARH+4/saAq4BMQCvA0kC3gC+ArwDjAHFAb0BJv20/KP89vyq//MB5AIgAqoEkgUMBNAHCgkrBPEBVQL7/vMAvgO3ASwCnALSAsEEaQJZAyMENwAoBzID7fxcA7X/A/x8AigAaPxFAMT9Of24BAcBQgQmCzID3QDfBeH/EP9TBBX9Tfqm/nP5wPqSAcz7S/7sAsr88ADGBq0BrgR9BTv/bQbKBbsB2wSUBVYA7wCjAaj8af+xA20BTAHfASv/qf/4AYIA6wLu/2H9OgDb/nUAqQJgA+D/nf3N/jb9rgJRBlgDlwS7/w/6C/4d+Tz4TP3++W/53fp0+cf7h/3L/LIBhgEa+8X+xwEt+6/97wCy+un7bfy9+ML6AfqC+O7/Wv9Z+tz/Zf7v92D+Jv8Q+839S/wE+Rr5mffD+Tz+fv7i/TEBSwCm/VgBFv7K+2b+Sf6lAR0Cif2x/Ej9D/lK+zv/wv8xALz/UACJ/+H+0wAmAygDlgWVCTgLwgb7BgQGcgXNB6AIqgjlBfYCiwFdBHgENQURB3IC3v+rAdkAjAJ+BVcHnAfsBo4FmAQFBPQCgAJQAOv73fw7/x4BhwFhAxQD5AH0Ag8DbAWtAjj+Pv+d/JT8bf84ACP+7/27/vf+nP8hAUsBcf/Q/p/8ff2L/2b9I/+9/5b76/ymAF/9lP2bAdD+mP5rARAAPf9L/rj+eABW/0H/+f47/lT/w/81/8D+X/5d/ZH+if5Y/k3/OwGc/07+Wv/zAOT9Yf9g/XD6Hf5L/lr/xgBj/XT8/Pz4+7b9fQDD/4n/Pv67/6T/QgDU//v+tv5d/o0CDgIqAXsC6wB/AKj9jfuo+1f8BP7m/jgA+vxy/Q0BrP+pALD/A/9e/5z/U/9U/8b9aP0r/TX8FvvF+pX7nfx//QL9jfuw/BX9ev5iAD//2fxm/cH7Hf66/Fj9c/+l/ln+CgBFAN7/lQH3A0cCUgB2Ac79bgBQAaoCqQfFAzcBHgRLAXACOQKvBDMCAgJzA/sA7v/C+80BRgGZ/xsENgJ4A9QDsAOlAR8EFwD//iL/iPzT/asAlQOqAogE2wQmAygDrASIAnAD5wIhAQsEegP/AncGUQTIBA8F+QDDALH/3P1XAs0BhwTfAbcB///l/7UDcAPeAeX+s/q7/BD9mgKFBqkBVgE4/3n/yPw8/s373/xL/DH6bP2F+df7svxo/tz/ywBtA8ABRQOEAVwAPAFBAoEC7AI7AjQB6QCbAUwAqwEGAGIAaP1Z/sr+zgAJANz/E/vd+uT6efuV/dP64/kg/Sv3vvwS+4n90/5O/aj9D/sh+xL7H/wM/WP/1f1l/Cr85/mx+n74o/n4+JT6ZPzQ/bv/LQHNAgkCLf96/zz9+P/d/uP7l/w7+Ib4ffyn+z4BcQRXBE4DfQJV/90AgALhBBMFkAQ7A5UCNAPlAvABtQEVA1QDSQSJCYwHdwgjCREIIAvJBs0EfwPHAWAD8ASoB1MFggWRBdICdQPZA/MFuwcxCF4IEwkmCGgGIwP1AaD/TQDHA1kE5QSLBHAD5gGm/+3/2v9iAIb/NgM/A5EEhQRLBNgDJQHIAE7/8P6AAr8CBAWFA3T+2v3L/Tb+EwAgAr4A1f21/+z/6gBwABkCMwMXApgAJ/+F/0n9yv/CAOv6uvmK+If6yPxQ/q7+Nv0C/YH7bv+n/8n9zP5//Sr88/wmAPkA/ADx+yj77vd7+fv7rv8kABv+k/2o+gX4kfhO+Qn8Mvyk/Pj9af8Q/O39rP2v+0r83/wq/qD+ff67/8b/PQDK/rH+zv6z+4n8//2L+3H9Hf+x/1//ef8mAJj+R/+u/uX8BP7E/nr/BgJJARn/p/0Z/J74VfmB/Jn/eAB5AYkAy/4U/twAeALvBIoEvQGOAJT/UQEoBdMDmwLH/yD+gv6v/ej8T/4C/4IAQANcAnsB1gIEATYA1gCM/4b/gP/R/iv83PoR/df+JAHrAWgDZAWpBM8FFwfXAscC7AOgBPEEYwIEArL+Mv6c/xQBBAQ3BUEGXAVSAXX+g/wb+1/8If+AAHEDTgOZAtsBOwF4AsABJgJlADYB3gI9AxcDnQNXBD0E+AQqBe4DxAOdBpgEQQPqAZH9yP1YAfkDDAW+BEkBBP/S/uIAJAMxAx0DBwKH/1X/uv1j/R38/Ptu+9P4NvwN/Rf87/2H+/n7ev2j/RL+Mv6G/m3+DPy/+6X8JPt3/IT81fhO+ur7lvuN/JT7dfmy/dr8qv1D/m38t//E/kP/7wGZ/1z+cf6H+4P6M/0q/VP+ygC6/c3+8P97/0cCLwPOAicDpAGY/jb7ef0R/iL/kQI7APn+9QB4ALUC+QbtA2UFyQL0/iQDHwNhA8sCpwO1/2b+LQCRAPYBxQMJBLkAuwFWAVUAUQXzCOkJrgdzBzoAAwAnBYsCwAhjCDEDHgU2Aa4A0AM3AFoC+wPY/ncB0/9JAKQE1gSCApICfP4K+nD8JPtz/GADzwDwAcwHZwN3AnIFNADJAZUCif4IAs8CSvwdAYoAH/m7/dX/3PyIBOwBIwCXA7UAff6d/k7+qv49ARQEvv9u/jX8DvsD/tT9kv6jACv/9f7B/sr9Lf7/ArADLQSJAP/9Mfvo+DP6Hfx1/W3+JP7I/Kb6tvrg/OP7l/4Y/cz9fv8YAC8BLQEa/+z7h/5v/jT/6P8+/Uj8t/wH/cH/IQCfAAf/p/z5+jv69vsJ/r3/tv4K/mQA8AAnA7sAmv52/OH60vwK/nz/nP6h/wkAyP+2ARX/mPz4+0v7Pf0YADsA0QEYA2EBXwUJAn3/vAFRAJb90f69+3v33vnD+Wr5DgAn/8H/bQOJBIYDcAKEABYA6P3NAKUBIgPiA68GgQJO/+79//tA/O//hgLZAqMD9AK6/2T/CQNDAb0C4QL6ALQDQgNDAh4EyAHUAOMCwQJHAxUGPAWfA/EBUAIMBKUGKwhfCPcGsASJBSkCgQIRBYIEBgaZBBkB6/7V/Xb/EgEjAjoBQAFjAzABywHzAAAAiv/p/4v/BQC//r8ATAETAnQBDAFdALD+9v6m/jj84fse+1L6Fvsr/K/6hPwJ+gP6cPgJ9273Kfil97H4y/tS/Af+tgAHAEn+wv8V/Wj9df78/jEClAK5/zX/7v1m/W/9a/9z/cP8l/4h/Ur+JPze/1n/+gCOAoICTABSAO3+6vuU/oX/nP3Z/Qf/E//D/9cCcQPPA70DowJjAKMAg/8CAa0EXwQkBJ0D2QCK/5L+DP1v/oX/CAFQBB0DHgWFAzMGnQMMAnUACf66/Uj+df1R/FH8DP3k/fUAFAOV/x7+C/6a/jD9s//U/CT8e/xb+yv+CP6h/jn+CAELAwoD3ARYAUT+Jf6d/lb/pAJ+BGcCSAKoA2UBsAKTAlkEWQIPA//+zP1l/UwB8wG0BlIF8QVQBuIEKwM3A94BeQcjBVgGpgMsBEUDlwGFA1kCQAL1AVcDdALTAy4EIwP+AUsAJf8k/cL/SQHJAacC8AA8AY0BdQJaAgwBRwA0/q3+bP7a+lj7Evwd+6j+8v7k/YP+cP7T+yP9zv2y/Tz/6QE3AZsAOQGLAPL+Ef8WAGb/IQAwAWL9KP6B/NP7k/++AJ4ETgRABFEDJAL7AaL/HP/P/sP+YgFOArMCqAK5AOT+hPzq+pf73f3rADsBvQCu/8T9wP4c/8f+BgCx/xYAWQAcADf+q/9OALIBjgKzAnQDAwIuBUUE7AFkARX+Rv1U/Er8bf3H/R/9Tv0O/VX7gvkg+4r7kfxw/Dn9N/6L/s8A4wC6/gf89/jU93r5/fyg/yb/2f94/y7/WgBUAdACwAO6BLgB5gHP/U780fm4+xP8lAIrA40DiP8k/5D/EQBABJsEVARKBFoCQwGVADIEkAKMA+IBNgDy/p4AHAG0/yEGmQNyAugCX/6s/38AzQFIAssAWgKx//4AxwIpACIDJgSTAdUD0gF3AmICgQHlAKv+bQHHAXcBFAWeAQf+Ff1u/AH9EP5V/sb7G/s/+tf71/vp+qv6yvm1/Lz/NwLQA0gBrgFRAooAqv9k/6H/qgDY/4D+IPyK+ar77Pl4/Kb9Uf+1AHEBtQEU/aT9bf4b/b//sv5U/1P94vxw/Dr69vup/vgAkgP9ANj/7P7D+sT8b/8QAtgEjgYmCOsEQQQwA64AYwEFAREAMACc/sD/awFbAwoDRwNDAeD/MgAAAqMEBAOzAin9VP3O/A/9Uv76/qr/gAAXAUD+Bf/3/6n/gQDcAXgAYQHRAVIA8v8lAvcCeQQWBGgFvwNmAzIDXgC6/d79nfzr+if/Kf4VAwEF1QJ1AVb/AgBf/xIBWgKK/ykCxwGCARoDFQIrAdMAeALhAsICsgHKATsBTP+Y/5P//ADyAp0EHwRcBPj/yQA3/sf+BQG6/4UDGwK+AFMB0wAuAbgAxwHr/xsBbwESA04GRwXWBAQCFAEo/6f+1v9n/vsA/wGHA4wERgW8ABv/A/55/PP9Jv/J+pL+YfpY+5H7Mvoc/S7/iv+1/Qz9LP30/Z7/Lv+R/TL65/sd+Oj5K/wY/Ij/6wFzARL/FP+8/Kb8uv5T+4X9I//f/uz+LP/z/CUBXwGt/1QCLPwJ/fj6k/kC+/n3SvqV/Hn4bv4kAAH+/QDuAI76u/qt/8n9WAHDAmAAjQEYAcT85P7s/9sAWAOQBpQF1AL2ARcAev/2AK7+n/5G/dT+3v/3Ab8G2wUKB2oG7QKWAlUDPAVMBJUGnAQhBR8ElANrB3YFGwnTCIUImwS5A7cFkQXfBQEHggdUBkYGKQjxA7kDaQTUAa0EMAPdATwBmgBZ/5IBDAXqAzgGKQWZAFIBGvyu+q/7C/3F/on/C/5N/Wb8mP0x/+f+Lv0M/2v9af5p/o/7Avxg+rf6UP4I/t795vzB+1b6Ovtt+mv8Bf2H+qv7YPtF+in7KPzX+CP6qP79/SYD8wFc+kL7OPn89yb98P/k/eoCqgKM/vgAuf96/CP9Hv3h/ZkAfP/XAvYBPgBCAXr/3/5K/6z9ivwU/KH+eQD0/83/Rf6O/ST/jQLS/9wEygO5/yYDx/1P/KH9Hfzp+8oAWwHBAjwCAwAz/wf/8/8tAeUApQDBBY8EsQNgBP39LQAaAn7/DwMjAin/aQJ4AKn8EAIZ/+T/ZQOZ/gn9Cf5G/Db/3wK2ADEEXgTOAdkD7gE5/48AeP00/WH9C/31/TX+EvxT/QL9ef6n//EATwApAdYA/ACNABgBqgHKARkBNf+f/hr/rQCCAncC9ACS/8MARQD2AhAD/v9pAlMAFwC8ANwAaQPEA5sD6wMn/5r70gHLAVL+CAg1AwUCIgbx/yL+7wEb/aD8cwGI/rIA+gKTARcAVwNbA/YCXQGVANf+1f42APgAoAJYBCcBNQFJ/xj/WwBvAFcBXAOi/+ABEgMpAdsBdgJvAZwCvgNWA10DWwRrAQ0Cn/07+vv7rPgm+4T7IPrw+oX7cP00APL+Lfh9+0X5q/rG/9/8Av20/iT7n/zS/yn9QP+sARb+Yf5h/5r77/7+AFz+9ADrAtUDLgWoAgsCqAH1AY4DvwNvAy4CVAMbBIwE+v8XAMgA2fpSAB8AiP8qAzwBOAHVAk0DTgJzA4MBVQFGAHj/Jv+y/GT8wflm+QX8Df6HAc0CKQJeAqIAjABs//H+jv1E/qH9LP7SAXb/LgBh/kT5NADDASgC6AVkBCr/fwBT/on9nf/O/3gCDQX4AxgDdQM/BDYFSAXiAzgDzf4Q/r398v65BNMCjgN7BJL/7//q/yX+3f1FAooDUQW7BgED1gP8BCICagTlBKoDrQRqBREEBwK4ASIB8f63/6X+x/16/Hb87v3j/5UBwQFdApcALgHMAU3/cv8i/vn8V/2c/xwBNAHMAQ7+qvsC/w/7kPs3+7H5lvkB++L8JP1c/l77hPm0+sz5y/ve+pj71vrS/Yf7Q/sR/Xr6UfzW/u34m/ga9rX0pvUO+/z4tf3I/gL+RwAs/9D8Tf9S/t77DP2w+gn7V/4SAHsBUwD3AD0AKgDx/ev/A/9l/Bv9mP+R/k//ngFaASICAAR/ApgFGAO5/1v/tf57/h0EfQaPBPEDcQVEAB3/5f9e/ngAwgMiA5kB7wFXA8cBYQPmAcL+rADSAikCJgabB8YEFgNkAnUAlgKbBXYHKwbIBnAEngW/BWcKYgc2CO4IpgiNBkcIygTJBI4AKQPcAT0CNwCP/+/8qv/e/jkAgwElAqL+YAEx/wn7lvza/BL8i/7X/gAA1gA9AgEB0gEJ//D9Iv9u/dT72v6a/S3/1gGfAPD/Ef8LAGX/4f/T/hD7jfuL+kf89/wi/BL7Zf0D/7b+lAGI/2P+OPy1+jH7U/x0/8H8dv+L/Rr/rf4Q/zj8fv7R/Jb9yP6TAJ0Au/+9ACv8v/oq/CH+SQBDA9wD0P0T+8b5O/uv/l8Ai/+N/wQAewLx/9IAMf3X//n+9v/lAcz+WP6c/er7F/r1+sb9+/wLAXwBbQKoAesC8/1n+5L7Jf/1/5QC+P9QATkBWQGfAcAF5f0hAGP67vxx/cb/PwJ//4n+U/5A/N39FgCVBHkBswZjBHwBMwO6AukA2AQ0BkAGswd1B5oGPQTDB7H/6AIVBMgCuAIVBxsEYAQ1BmEFiAgQBnwF5QOQBNwBVgIHBKwAkQWOBLgDcQLrBPMEwgT7BwYFAgMCBHcACAHWAAoCQ/zT/mgA2P/dAx8EHAGQAc4A8gCk/5MAxf41/r/+s/tD+kv3ufjB+pj9nwCUAqYCmQBU/PX9zf5ZAKL+oAG4/rL9K/6E/73/CAFmANEA/gDx/mj/UAEe/dz+uP0W+zT4/fge+UX4E/xn92n3cvcQ9j/1ivic+GX4I/sq+uj46fgO+Tj35PiD90X5ZPh7+mv6d/wr/fb8K/18/Y/86vwx/Nr60PwF/WYBqQDZAM7+KP7X/Q/+Yv+S+wX+afx8/dH9u/0KAKEA/wLeAgUDtAOhA3EEhwJp/7z9h/2kAYAD3gaMB8YHcQhjBYUEKwP8AyAGuQOaBpkDlQWKBkgFEAaaBHgIrQQZBCgDnQI/BNUCqgRPATkB7ACVA2AFeQXCBXoGSAQPBNIGTgV6AeECYwP6AvoCeQLZAIn9XgAs/6r8Q/6z/V/8NfrH+nf6tf6qAUkBDAG+/9L9TP84BNIEzQKYAm4DVQCvBOYD7ADK/TP+X/4MAPADkAS+BG0DlgBvAK4CfAZLBv4FaAQuA2sDqAJIA6EEWQPZABQDCAKO/iwAHgBS/u8AwAGpAcL/qgIT/pz9qP5d/ZX+9v4XAMEA3AFP/yf8Pv5P/cf7b/sL+bf3OfuO/PT+ff4zAEr/zP4r/ST8z/v2+y39vf31/JH7Pvp4+y/7L/yY/JL9Hf61/kYB4/7Q/TQAJ/5d/Mz9+P0s/Cz/cP9o/q/+Kv9e/o0AMP2r/W79e/24/wD/qwF4AG0AngBx/qn/wf/WApz+/v8UACP+/v6q/c3+IP7BAYUDzAAvARb/1/zm/ZMDfwOJA8QAwv9l/+0BvAOwAx8ExQHdAowEswTxA28EuQKiAXf+7vpg+1P6S/12/ZwAxP3Q+/b9SP7j/CT+zQC4+6v+EgAq/BL/YQAbAlsD2QUHBIICgAYRBIcDuQWOBigGGgSaAyEB+wIqBcMDgQcwBeEGjQcgCTEHGwN8BXgC5wL9A0sB1wFlAb0EyAGfArEBHwDxAJsCywJNA14D6gCo/d7+2ACh/xf/WP46+hP+pwDL/2H/yf/W/YX7H/+L/ToATv53+yb8/fvV+nL8I/9H+pb5sPu792X61/xx/Tj/CP5B/fr8tPz0/eH89/vP/fj8vvvc+g/5jPZb+Tn5WPr9/B37N/0s+6L7Zf6L/Yf9PP52/7v+tAAzANj/FQHY/9EAzv8eAKECygXEBvMGrgYqAqsD5QPYAUwEegP3A/oDqAUQAioDDQVGAPcD6wA/AHgA1wEGBhMH/QfZB4YG+QKuA5wCEAElA94C6QHeAtQBNgP4BJ8DlwJ9AbEBRAH6AkQDKQOEAVAE7QNYAYMEDAFIAFECngMNAZ4CgAX7AjoEhAIo/4/9jf7XAH8BjgBW/sr5HfxP/o/6pP1T/rP94ACLAib/uf6+AGL8r/1Z/iD8gv1A/BP6yvk0/EP8G/5Y/hL+qPvM/Iv7RPmL+lD5C/pn+LX7jPke+VX9uPqj+SL4y/i/+lP+Jv9Y+9z70/qC+1H+Cf1w/cv+Gv56/wsAcQFV/7z9Sf4s/gv/DgBMAqYBTAFyAbgAJgNhBCAFFQXNBP0DDgFLAV7/S/30BJcDkAOBAjj+ePsQ/hYBIAHwBlABOf4I/6b71/9f/tsA3v5IAsIAcgD9AqUC5ADs/ykALAAsAbAD5wKLAAUBMwGEADMAzf+P/yEAGALUAtYE5QVCBqoF6AQJBSYCpgJiAt8BwwE2AnoBd////5j+qP/u/uYBjwMxA/gDcAHs/yEBSAGiAO4BQAN3BIsDtwJ0AHwCpQJrAg4FHQW4BBEE6wNx/57/WwDP/pX9e/98/k79Df33/HH7jvtA/pD8EvyW/CH9+v+0AnsCIQAP/4D9h/l9+3P95P3UAMv+Ov6d+gz82fvl+wP+cvvl/Rr9fgJ1AJz9PvwH+gT+x/3iAYcBNwHDAc0DagJMAkAExAXcAywGTATjAPkCAQFLAjQCyQKxAecBPgLu/k8BSgBj/7v+v//D/s7/JANGA28DrQLE/6X8X/6j/Z4CmwS7A4wCc//8/zIALP+8/tj9Lvzw/tL9SP2S/R4Aef6Y/4//R/1a/uL7nf0p/JX7zfym+0v8B/+mAAwCeQIoAqcAlf+5/vn+4/0x/k/9+P0r/DIAqv4nAD0BNANlBKEB1QKj+z7/Mf6cAAwDgQKjBFEE0AeEBM4E2wM8BNMCpgWcBBQEkwGGAC4BfwKkAxoA3QOVAAMAqf5j/3z+iv7pAJ0B3AGgBNEDCQOiAkECRQKYA3EH5wSYBff/zf9g/iIBp/2K/JH6g/ob+Ur5Vvs+/PP/AACy/ub8X/4N/279HgIh/hkB5gF4BKsBKAIoA1D/EwRNAV0E4wJ/AY0B9AAqABoBnAEPAcYAZwEfAjUAAwL7AI4CsAFqAKP+oPw8/w7+Xv+K/bD6X/d79+n2J/ad+o/75vxp/uD+Wv5w//b/vf/h/jb56PkO+bH7Lv5b/Wf/9/2+/aD86/mV+ND4mfoy/Lr8Zfz4+wn8OP3A+7v9CvtX/U78WP6J/rD9yf6f/ar+Tf9VAAQBif6z/ef9Hf1n/jsAfAEUAj8BOgDM/kX+hP5wAyMCHQOaBeABJgAG/kH/4P6zAbQClQNuA5MExQVlBbAHGAYyBtAFmQaKAiMDCwBEAoMEKARIBP0AvgFj/wsBff5//D76lPwwAeAC2AMgBLcBRgLpAT4CAAGeAo0CmASXBXYFEgWPAhIA4/8aAc7/cgKA/gn8Y/pI+8D72/wBAfj/Nv/Y/aj+YP7x/in/I/1B/8D+iv4CAKYBEAGIAG7/bf4p/QUBSwBsAD4D1wJhAjAECwMnA6UCfQILA50CXgXrBJcERwVeBcEDdAOUA+X/lgIGAoIAfwBQAXIBDwISBL4BzwB/AKf/VAHCA10DBQKGBLgBUwPjAcECtwWJBqAEvwNAAjYCUADE/40A3P1h/2P+Sv3q+zn/3QDRAfUB5QCYAmQAGQB3/xAAHf7G/K78OvpH+5H7rv0J/m76v/tB/hkAbgMSAoQAFv90/If8Sfw7+sf7vvuG+2/8fPxs+rn6k/sN/aT8Evwx/Ob7APwn+gz9ZPpi+xn8EflU+ML6Ovo++bf8E/z2/Vf/1fzs+jX9ovvT/AP/Xv6G/h3//v+Y/vT+/P5l/nj+DP4N/Y383ft2/Z/7C//DAt8DvwQUBVgCkP4fAPz+sv+6AKz/OP+q/Bf64/fc+Eb7UAGBA+4ALv7v/xf/ggEMA7oDWgD2/80ARwH2BQkEQQOBAo7+Av4+ADQAgAJxBVEFFARmA4gC1gEWA0QCIgP6A1wFBQUTAwAEBwMlBH4CTAMeA+gBfQOtAtoDFQaBCJwFrwWyBwQIzAq4CpUI1Qd9B18FYgUMBFUDDASzA68C5QN6BFIETQd7CFkFqgbbBsMEQAZmBlQDawTRAyQFxwMdAUoARgBM/xMBzwJm/74Amv2y/MD/XwAzAFwAIgHV+RH+BP9v/Aj/IgB++/r+3f3k+sn9qv0f/OL9zvpu+Fv7dfkH+F36p/gF9zb5N/gG9yb7b/pv+Lz4m/dY9zb7RP7N/BX8BfzX+lb7O/x0+uz7f/5OACsAjQDW+sv4cfre+v35DPv6+9r7lvua/BL+s/wL/hX/RABQAE8ABAFh/m3/Av6x/n7+ff63/uD+t/x/+5L6x/l09//6S/yW/xMDlAFb/zH/agCiAFAC9QKjAeoCgASIBYgIcQgXBzkFJQD+/L79GwC7AdMEkgM1ATMDGgBLAs4BXwGPAL/+wf4b/9EBjv+TAH0BJQLnBH0ENAKFAQUE3AJhBJUDvAFVASwDMQQ9BUsFVAI/BJIEnAJuA3IC5v/u/kwA2v+qAKj/rAGj/rsAmAD3AKsALQLgAMz/sAIJAHcDagMMAxoD8gVjBYMBwAON/339lgHKAa0BawTu/7f+uf0s/YL8y/7V/Vn+tAPJAGEBjAH/AH//YQJ9ACf9mP2D/Ur9ggB5AYMAxQD5ApgBbf45/Vv86fy+//UAlQCcAEoBUv+j/6H8QPxo/h4BWv/g/kH/V/6c/Mf8BwH4AZ0E+gJoAa7/L/9IANIAKgKd/l/+if1U/Xf+Cv6V/JD86Pyj/q7+rQAx/lj+Kf/X/j7+R/+y/zEAMgDpAGX+0P0ZAPIAkwDwAD0BagAAAPT/NAAsAe3/oABrAvoA1AEJAo8B3wCJAVQBiAHlAuYDeANCAlcASf+x/Zz8cfpY+8n7VP4D/2L/ff+Q/PH89vqA+yD7Yf7d/1gAvgPlA+gFhwZ5BnkFagQtAjcCkQDIAC8D/wEnASX+yP4b/w0AhQCVAvYC+QDsAT4AaP+B/0UCKv8pAGAAFgBx/lgA5gIeAU8FMAO1AqkAfP+C/rL/2wHiAc0BV/+5/NP7i/9m/2oDsgN0BZYHzgXgAxX+8/1T/lX//gHXAa0BtwDDAeUB0gLcAjIARwAcAIQA7QFvAsX9ufxY+x/86v38/wUACv5U/pv7gPv7/FL8BP4wAH0CGAEpASv/uftU+kL4JvdW+ZD9yP8u/pH9Bfuf+Ij5nPiV+Tn6+vzR/qn/fP7t/Ov7M/3I/Q7/QgDo/1cA0QAeArX/T/+P/e79N/7R/dT+0P3qAOf+EwAyAqz/2f4f/rwAzf4vA2D+i/+xAGH/3/67/r/9HPwG/Yf87/sh/TEDaAOsBFgD+wLwA5gFzAXwB7kHCwZMA8UA1v5AANMBdgRdAvcCMgLGBDsFVgUhBBgBlQBAAZ0CTgLfA10DXwKdAn4DZAMDAe8B8wB7/1z+w//k/74DWwGjAcH/gP/dAEECvgN/A8gGdgRjAPP+IP/t/Gb+ewAWAUEC2gP+AmQAIAEr/3EAuQFJAqv/qP9u/9P8B/2z/Ej88P/aAbgCkwMVAjgCuABkASYBMf/K/wz9Xvwe/AgB4gLlBL4Bcv1Y/Nb4w/tj/Zr/kv6I/wT/Cv3W/EH+Ef09/Rj/Rv+C/Vn9hPwS+x/9Ovru+Vz8rPqR/NP9YQATAIUBhQKVAo0BPQLeApEDegQYAYL+Qv3s+v/6t/1L/Fz8mQCv/3MBpwAx+yX4kfUp+hH//AByAVX/l/62/k//ev4q/hH/lf6eAAACswO0BWMEdgPwAcoAkAFoAqgC8AEuAaAAOADy/iMBSwBXAWwBiAFw/yP/6/67/oYAuwAzAyAC2AT6AQABxwJ8/v3/fgCjAO3/uQCXAIsC0wMSBZwEQANuADb/pQCLABf/fv7UATUAqADCAeX/w/8S/wP/sv6w/ikAav/W//AALgDuAEwBTANcAVkEpgDMATwBJAGE/oH/kAA6A9ADAwMgAosA+QBC/z/91f21/YT9U/zV/nj7fQHrBGMCtAGwAA//IP4jAi8A1wVrB3gDGQQR/977mvvt+5H9WP4a/lH/+v2m/xYBKwGcAXYAWP+UAeYF0wSKBoEFQP9Y/8H+Wv3f/6kB2//x/9f+HP5+/if/mwDUAWQBsQFeAuH/BwBG/yj+/gA0AmICOQA8/UL9Evwy/eD9LP6a/h3/O/1b/mf5HPmg97v5GfzK/T0AEv5k/pT97vs6/gz/5P0Y+7j/+PtU+8j/z/tk/Mf/3/0FAXsCVgDNA+EB0P68AZgCL/42ADQAdwDqAU8BVwThAGQCowMiACP/0gLs/9cFWAfZAmsCgf+F/okB3gPr/q4Ayvr4/XL/mP+QAIYAswCVA7gDIACb/1L8cftJ/4P9uv8iBBQEBAfGBxgGPAckBq4AlwKWAEf+IgMrBOwAqAK0/zb7gP0t/ML++AAMAKL/8P1b/sT8H/xB/Af8Afy7/Gz///8OAB8Bav+EAPEA0P9b/YL+Cf9D/pb/Zv82ACYCOQREBDADBf8PADL+rf9MA8IBRgQHBMwAYwDyArYAfwA0A2L+hv9wAa8BegW9BqcErAIeBNz+cv9iAUL/YAHtAvAAoABdA1L+F/9EAYcBQAJtBXT/ZwKBAHQAdQC1/iP+Yf8GAHL99v02/o7/cAFgAuoDCQELA4n/tv2I/6P+4/92AjUBk/4H/93+EP6JAF7+VgDAAsUCqQO4BAsD5wUbBusDPQU3AjIBHwB7/7D/0f4D/rsA4Pz3+x0AbP3E/uoChwHw/YgCagCO/m3/P/8P/eD+TfwT+2z8wf3I/E3/Tv8P/FT9ef1f/Tb/cf/e/qr/1P9ZALH/FAFUABEB8wCC/6P+iP5bAe8AKgOXA68B0f/J/bEAZP/xAaQBWAD0+ln42fnN+sr5wfxw/ur/7v+KAZL+oft0/A77JP1K/kL/N//v/t7+/f0hAQsBqwL1BG0DzAJjAK39IP38/er98vw+/EX79/t7/ib/P//p/XL9HP5R/X7+Qv0p/Yz8xP3DAF0AQwDo/Rf8rfpT+s36D/y7/ZL70fvb+5X8+f5nAcoB2wA/A0EDlgTJBP0B+wF+AUEASwBzAW3/QAC9A50DYAWHBgQEPAA6Al4DoQRjBJIHlAVABG8ErwWQBIkHBwddBRAEjQX5AyEAtf65/EX+7ABIBJkD5ATBBXUAfQJL/gD/3v9XAIf/MAPFBMgEAAQaA78AUwBAAr4BqAHdATkDZwOR/3n+k/y2/ZP/5P8/AOP9Wv55/XH8tPw5/3L/AQBZAef9tvyx/Tn+7vzV/aj7F/tb/MP+8QH8ApkBJv8r+yn5iPfS+RD6HvzK+y386/uH/KD76fvd+yL8w/2m/RD+wP7V/S39TPwk+aj4Z/vu/mgBRgInAuIAHQL+BJcFyAUgASoA5f0t/F375Ps7/x8AIwOOA7n/Qv+J/54DJACiBQME9gK7AtIAEv/G/5X+ofmM/H/9Rv5R/xkBz/9iATsENwRfAwYDdwFKAecCKAP+An8FEgOfAqcCcgO6A8kDfQNCBN0ANQNKBXQFpQV+BUcECQEK/nf9vfzw/pwBhwGAAQ0A7v89/5T/qP4w/Fr9HPs2/jz+gf4C/FcAoAK4AekDMgHF/2j/i/9kAOEC1QPpAuMBAwH8/nT/egDjACQBj//B/xQCPgTYBOEC0ACv/1b+eP88/8UA/ADNAHUAD//X/of+EgKf/zb/JAJM//0AJABlAJ//MwETAZj/VgDrAFcC5wFVBN4BUgBe/h78ifxg/jgAYAFuAWIBUv9/AJABQwAw/2P9R/yh+/P9zf5a/Hn9KflK+4T9KPw0/Dn+B/21/RkBwwC1ASQCiQNbAnj/9f0F/Lr91/+KAksDCQSvAKL88Pzv+mL+HQCR/2UBDP9p/fD7MfwT/jsClAYrB/MGSwUmBFEE/gIIAnICVwFfAKYANQBj/9b+PQAYAKcBVgNhAtwAhf69/lH/ygBVAGwA4gBLAG8BMwGNAZcAK//7/ab9XP8dANwBhwRoAzwDEAOBABEB0AHYAZcB1wJ6AjwBHAFB/5b/dP6l/l/+sP3M/Y39K/7S/dP/gv89/goBCAB3AAgBfwD6/nj+tv7o/G4Azf1Q/Rr8hPpc+cf8e/wg/vr/HP8sALX/tQDeAPP+cf5G/hr9y/xwAQ39qf++BAoDcwJPA7b/dPwNADf7Pv2p/qH4x/t//u76q/tO/tD+4AG3AzwDmP6j/aD+8Pz1/o0BOwCQAeICbf5c+yz+l/p4+W78W/pu/XYDSwQcA8ACmAB1ACoCpgHS/4b9DPy2/Nn/8ARAB7kGUAaYAtv/lgCI//L/HQCS//EABABEAAABpQCvAnIC6gBtAboBL//XAdEDbgC3AEcBtfxu/IP9O/+kAZ4CIQI2AywEogF3A5AEZQBHA9kFDQNZBpEGTgS4Bd0FGQOMA1ECIQCWAR4CDAGdA+YBUQMLBPUCJQBrAfsA+wBCA3wB/wEzAH8CzAHnAckCx/2p+zb9iv9EAjoHPQHh/b/+rf3Q/psE2QFt/qYBc/7T/Wf/p/8UAN3/6v2L+0b5p/kA/o4AzABqA2AAlP+AAD//h/08/Jz9CP0R/rr9L/sT/G/8pvy1/Lb77Py8/O/8u/2u+136nfsk+6v7xP0Q/Qn/zf8D/bj7ef0X+U/5kfYQ8zX2//TQ+SL9//wt/uv+AP2Z+xT7Xvc5+PT5w/jR+8f7Wfwk/4X+kfy4/mH9w/xW/UL+jP0hAYgDHAO+BQcItwcSCUgKEAYMCQYGMgbPCtYJKwk9CVgJUgVfCRMICAaQBtAEsQPwAn4FQgUtB+wI2AZDBdQC5AC4/ZT/HgEBAMQCwwB0/4cAl/+MAC8DbgEiAj8CHwK6/+79PfzZ/Jv8+Pyy/sr9q/2S/VT8Fv6t/7P/gAHhA0wCxwGeAukAMgAOAGMAYQKsA5QEJQZFBu4B7gI7Al4BKAOxAkABEAAHAMn+fQC5APT/OwH5AR0AVgDS/uP8I/5W/fb9tv3B/4H/mwAlAIb/HgFzAkUBEgBO/uT7bPto/ST/AAApAqkB2gKFAaQB3AIAARECtwD6AegAIwKWAWYA9P7J/D3+//1h/jT/7P0i/xkA8P8q/xz9A/2G/Pf+HwACAAIBYP/3/kr8Qvt7/Mr8hQCy/vwAqf9nAL0AcP37/QH7Afzp+pv71f3O/f3+wv5fAfIA4wIsAL/9j/mA+rr+8P48/+X/H/+y/psBDwKwAIECewTQAuADmgDV/vL9gwCJAzECLQIz/fz8yP75/xIAIACvAOX6//xM+3T8mv1oAAACawGbAS4CqP5A/3r8L/0L/lf/If+T/3T9zf29/3oBwQM3CBEGVQUHBbIC1f+m/uj7ivtA/kEBUgLlApkB1wGQAC0CKP8X/Vb+0f7rAKkBqgBJAMkAJAJ4/0sDGgboBRoFugJe/QD8/vwnAB0BRwG2ATD/9gDf/xEB2ACd/wsA3/8GAbUAYAIoAp8BCgE9/l3+yf0hAQoCAwO1A5oD6wOjAnIClgBcAFkCuwOkAgcFGgYNBYYGWAWtBN4CwwLZ/ykAcv3H/E3+QP1w/1gArv/JAOgAdALzAHACGQA9ARgAbgBS/wv/Zf7K+pX6e/pz+hf8BvqZ+kz5/vz9/aj/9P8QAA8Bsf9bAv3+pv6D/0r/wP/k/Z379fyL/oH9XwDC/sv8Mvwz/I3+Yv3V/iT9l/wH+vj43Pjo+Or7RQALAcT/ifxE/Pj80v0F/2f/ogGmAi0DNAPUAYcA1P6Y/2/9Xf7f/9gB4gHYAwsDIwXeBcMCYQS1As8E7wNWAg0CTf+zAOQApQDA/oP+QP5+/40Bf/+k//T/dP5J/14ADv7W/Ev82fqi/Y79K/4G/jv9cP2f/ksBewBu/2392/vm+tH8Zv6X/9P/Uv/b/ej8sfwZ/SH9lf5v/c3+OgAxAY4BDwFR/2v/PP/aACoB6v/K/zUA9wCqAiMCsgOTBOAFTAVJBNcA+P16/s78rf2c/A0AdQAiAZEEgQG4AYMDCgIOAugEHwS0AxAH0AK5Ak8C/QCEALMDIAQlA68BBwEj/2wBAQR7AZIBjAGCAZ4CYwS6AqAC1wCX/hT/DADAAEEERASVA5EDTQLL/6gBIgBF/y0CIQTZBG0GWQTsATr9z/1P/tv8rP6q/5j+PQABATz8e/1v/hv+tQFY/3z+uf9AAaf/aQE6A9YBowHpAIT+5PtV/MD9FABCAZQBMwA+AD//l/6x/7MBpALHA/UCcwG1AIAAHv+v/23/2P0bAJ3+ef/QAG0AFAPvAUIBtv6i/nr8w/vS/oL/owEIAQQBE/6z/NT9oP0q/DD7A/2n+hL8VACGAP//1wBC/an8vv3E/Jz8JgGQ/Tz7jv5M+j78KABg/U3+Xv70/LL+MAJNAOgCawStAxwCrwBCAB/8Tv1RAG7+g/2+/zb9mPxHAFP9sv3/+yf4xfwY/9gB1QJs/8z7s/yP/XX8I/9w/nf+9wDk/2D/tf2c+y78fvw7/d//kgHRAfEAx/+z/lL9pf6a/2oB3wJXBNcEdQSZBdID0wRpBOsDNAXbBW8FCgNaBakA9f7r/0v+eAC1ALv+kf+UABz+S/41ANj+wP8TA9ABMQL/ALcA+AHJAisFAwRjAeX9P/7o/ZL/gwTpA4QENwbWBCgF0gblBtgEOAaiAycBnQHo/1gDuAO0A3UE9wVYAfgCYQCyADQDSgSdBEkEwwNKAnkCd/02/cUAu/3oASUC6gDwAQAHzwPTAvMFtf/mAWv/cvvk+tH4NvgN/Ob/dP+VAe4E/wNcASYBZPxP+Xv6yfto/fT9Uf4S/Uz9z//1/sf/nwBL/nL8oP3O/s/+jAD2/hD8SvyW/UX/AwGCAcb/UPy9+vr68/xt/0gAPQDY/3L+X/7j/XL9Iv1c/Of8g/pU+Ej4Cfj7+aD68PpR+S37J/uq+fj8cf4l/sT+HP1a+s33PfpZ+0L79Pm7+ov7Zfy7/AT+u/u4+oD+YADzATwCPwMEA5ACvwOCAh0CGQHTABQBbgCaAFEB4QKnA0UEfgKFAFIBj/83AtACtgRkBGIFoQXHBKIF1QJEAMT/LwH5AcEEQwSiAicCoQOQAwAD0wVtBEkFCwc0B+kFqgVLBXgDwwLvAp4BTwIJAqwDQgLoAokDWAI3BIAEqgN1AdQABQFe//T/WP6r/Z7/5/9GAF0BKQM7A20DOQRtA9wB4ABaAC7+Bv6Z/Y39l/xX/c77cf5IAKz/3wG/AGoBPP+f/nf+nf2x/18BRAOIAycDNAJv/5P/GP7o/hj/fP6x/sz+4f4SAMcAwP9C/hH+cvxN/A39gP6T/40AMgHt/gMBBQCEAfUAkP7PAMj/pQD+/qsAwP6D//8A5f+HAJsAeAHMABACegFj//0BBALeAogAvf6l/B/8Rv+1/+MBWAO0AWwBQQDL/uT+a/7U/rYA0wC7AM3/6//b/kH+1PxU/Av9jf1d/ln/Vv9A/kv9Tv1v/+gAsQDbAZ4COgJgAIX/Iv9D/X/8TP/Y/tD8AP06/+P9kABwAJ7+4//b/k78qvsY/R/9uP7pAeT9u/mq+tb6NP4gAVMEewVqBXQESwDY/K36Tv6lAHwBW//M+4b4JPb/93P7H/3l/XX+df32/ZP9UgF9/6AAXAApAlwEqwX9BrsDnwF4/Sj9+v2H/Gz+5P6e/Qj/u/8YAAH/7f4G/jv7pfu2+4X+BQApAOr+O/26AHT/mwBo/ur9EP/e/5kDcgOqBYIFMwTiBE0D1gEj/h3+5fwyAF4DhgZPBwYHUQYLBBsEBP+p/L7+2AArAqoDuQSxAewCPgU8A9IDYgLS//z/gwFvA7cBygJdASD/jwDx/4kBYgIHBuMBeQGIAED+j/8OAhgDmwGoAlUCqv6s/Gz9nvvq/Cr9Wf0k/kr/BQFHA8ECtQMnA+oBuQFtA2MC2ACGAKcAKgEkBKYFWQYYBzcHlAdSBecDVgJj/6oACgEGAtIBEAFBA/0CPQJr/wgAWwETAGMBOQGyANsA2gGpAToAW/8XALX++v85AEwA3P8S/lj/Lv6S/LL7fPsp+0b87Pzy+8D8E/2O/+r/bv62/Zr7V/ms+Fz7z/of++b7iftN+cv6LfyI+2P+uvzs/d7+Dv72/Sf7dfl69j75bfhW+Tr8//nb/CD8t/5t/lH9evy4++P7uPsP/cH7sf4hAUX/vP08/Tb9zvwH//r/hgAHAYMCbgLWAagBVwHgA4oEzQInA68DuAJQBMYDpQR5A6kAyf8z/Gr8mvthACUCowRcBvoE6AMyAL3/Qf6V/x4ATgBa/hb8E/yK+eb7awCnAGwEgAP9/5sBLwHRAxgH9wclBEEE6AUSAh0FoQEa/+z89/3q/k8AAgUWA3kDTAVRApcBTABJAmEB3AGdAWoCMgLyAnEDgAMqBDUDsgOBArgBKwESA9UGSAVeBNwDngONAZz/PQIgAHsCZQFDAkMDywPrBL8E/AOm/8IBbQBDAGUC/P9f/5z/cv9bAGABRQHX/7MArf8e/vf92/w+/J//O/8r/6L9fvu4/Mb9bP9zAcMC5gGVAeEBOQDn/uP7FvtS+ub56vwW/Sf9Mv6//vX//wFoAeP+ivzA+Rn5RPoL/Fr8Cvxf/Ez7HPwp/OD9of6T/PAAIADcAJz/Cf8a/pP8CP65/T7/sPw/+6f8wfkE/Vv/lv+gADAD7wBo/xL/K/7V/iz+I//KA74E2ARqBVMBE/0R/A378/eM+Y/6FvtI/NL/xQHFA6MJ2AcICJYGQAIzATEB3AE7AaIBLAD+/uD+CAA3/v79Lv5cAWADywP3A7kAlwEDA80BGwJYAP0AWgMyBSwEOASFAB79k/3I/er+fgFSAvH/ywDk/sH/8v/G/ez9uvyH/iP+FwE0AgsBWALfAQ4CkQEQBG0FDwTJBA0CHgDtATIEuAMaA/wCzgCa/mX/sf4fAKkDLQTnBJsDzQC3AT8BfQLWACoAQgCrAMgCjAMuBs0EdgOmA/UArABVAC0DLAMoBdcBLv4Z/+n7Uvzs/G3+kv5TAVYAVv3u/Zv4Avha+zr8Ov3o/NT5WPnN+bT90f5U/7n+Xf/F/pj95fyo/Yv/ov8dAIb//P08AJ4ALwOgA3cDwAK/AvoCuwCoAesAsAHRAeQBCwIOAlQBnP1E/dH8YABqAnoDAAXQAEv/JP6D/mkAzf+CAYADGP/j/qr/3v+t/ksBFAFB/mQALADO/fr9uv+XAD4BuQHTAUkBjwGhAPwAcQHM//v+ev7W/rr/bf+UAIwBmQF1AnUBTgGO/6f/Pv/QAA4DUQTpBt4DLQJu/xn/k/71/ekAev80AW0AhQAV/h380v4l/yX/b//9AHkAlP4ZAMn9av2l/kL/ogFgA6sDIwLnAA0BVwBTAqMCNQKfAkkCsgOvAlUBL//F/7D/zf5RABsAYgBfAaUBOQE2/6L+wv6Y/vT/K/+z/nD+Vv5D/xX/av68/Lz9av6zAGcCBQRxBBMAOv6h/Wn9yPyu/Kj7e/gv++f89v7q/5n+MP+P/3YAtAC3Ai0BLf/B/+T96fwu/uv9s/tm/aL9FP/K/tX/OgHV/8AAZQLIAxkFOAWKBGoC7AHhAY4Aq/6O/bn/1f55AroCKwJKBLb/dgDb/Qz8GPvV+8n7Df7BAfAAmf8Z/rD8f/ur/Zf+gv5u/eT9jf4F/Iv81Pzi/J/+uv7G/kz/AQB5AFMBFP4u/3f9a/2l/uv9ivzj+/v87/vw/iz/NABjArgC+QM2BDMDOAItAVcBzgBdAogDqgIbAQIArP95AJr/EgEcAU4BuwA6AOP+Cf8YAH8ABgGPAGYCOgTiBeAHaAUBA9L/2/6zAEICyATLAwkFYwY3Bq8GNQeBCO0DLACP/M/3sPcc+2MCDgBmBGUDIv/S/rf+VwB4A9wEcABvApz/Bv6Q/hH/kPya/Qj/k/4q/WH9D/4t/aH8pvxK/Pf9LP3U/l4AVgBcAAkAnP8uAbr+Nv+D/y39Qvxk/bP9KP6N/Cb+Dv5v/+ABEwIDAbf9jP6W/ZD+Gv/G/Ff80Pkz/AP7yfoq+eL8NQCFAbgEpwLaAr8Bif/sAFMDPAMlBbUE3QFf/x3+Lv06/XD+Ifzw/Nn+gADHADb+Wvw5/Ln8Lf9Z/0YA/v1V/lwA2P9UAIwBCwOs/ikAlQCk/VEC4wC7AUcDmASRA+8EYQP6AVYCfwD6AfcBlQIuAqIBMwKSApYE4wXQBHAEAQK3AZICdQLRAwoCtAENAOoAwP8YADgAC/0HAggC/v/uAqwCaAA1AzADPgFRA3oCOgUUBowEBAK1AbUBZQLvA/8DbwTCArkALAB3ABQFyASUB6YIAAPSAeb/i/3q/+AEIQfoBpUFJgCL/+L+Qf26/h3/c/y5/fL+0f7w/jwAav7z+2D9m/wS/cn8c/t3/Nf8q/6X/0ABp/+aASwD7QB0AZD/n/wm/In8O/1e/sv/SP59/lUBSv74/jb/h/3Z/VT9S/0+/bL+l/wm/K/8i/pT+834tfdC+UP8Nfy+/QEAnvsW/dr9AvlD+/v5HPkM+jD82/mK/Sj+fvyV/tb7RftD/tj/hv72/136rPlt+1H8zf7n/ob/Sv9X/q766vyK/PL5ZP5jALUA4wGmAyoB7wBYAD39HQFJ/yj+JADf/vv9vQLMA1gAigIxA3X+FADTAMr+ugK3BO0AtgFRAqMCkQR+BuoDKQI8A+gBbwG+BTAH/AaYBuIEWABgAM4AjAHiATQDDgL4A6MF7wfPBZ8GxgZKBm4FmgeFBlsHXgVlBoQE8AOVAucBJQEbA64CEgTSBGYEEgPvBFsDawCQAQsBv/9RADz/iv/p/z8Axv8jAXX/HwCyAbP+wPxH/tL7NP5mApEBMQKMAoED8gK3AzsBTfxD/Fr6vfu2++/5iPdA+B75OPhq+w36XftR+037S/wQ/XD+K/zQ/qX8yv3H/bT9KPy0/Vn9pv1I/+8AmQDB//D/R/uL+WH7EPzN/jEB+wGy/e/8d/tg/I7+Sf5p/Fn8qPzg/p38Lf3X+YH7JPtz/Nj+QfyN/ML7L/po+XX7Nv+XAIAFowWKBZ8EaQXqAQ8AVQDGAS0CQQKXAI8BIgJ6A8UE+gSF/Tj9C/mI+yn+9QHtBJMCCAOgAQkBzQLdBBAHyAScBwQEygFyARoBkAAiA10EWQRvBGMEiAM1A70DWf+LAUYDDgJFA1YGGwWlBZAGowUYB9cD4wO6Af0BsAAFAnMEgAL4BvcF8AScA5ME4ATEBKYITQU4BeEDfQGzAIEAyv/h+uv7DP2Z/joDngSNA3oEKASvAyQD2QIVARYA4v+f/Gj6e/cg+dP71P7qAMABxACk/E343fh5+TX6Gvnm+mv5wPk7/Bz/QADk/xL+nfxD+7P53vkS/Az6YPw2/AD8yvoN/Lv7efrA+5/51/ro/PH9jv7tAF0Ax/4J/+D9efxt/H78Ufsz/Kz7RP32/CD+kf3A/o3+sv5Q/rL+Lf2N/ST9QvxT/jb/KwJ3AcEBSgA2AAAB5AHpA0UBJwJiAH4AhACsAJkCqgJmA94CzwHiAIYAwAHLAc8B0AFTARMDuAJnA9oBKgGjArkBSgOhA7EDmwPOAKIB3v61AEcBUQFzAXoAiQPvADkBUwBeAgsEIAReB+AEGAT2ARoDrgKMA/sE0wWLBaIEMQcmBoYD5ALAAuIBqQGfAi8E0gNZBWgEjAD4/kr/RP9q/i3/y/7b//8AQgAA//X8KPuN+5T/EwHcAPoARwMFAfICAQMnAcv/dgI7BBkFZweZBYMC1P+W/Pz7rf1uACH/r/3i+zH7XPxH/ukAMAKcAcn+aP5C/Z76WftL/Jj7if3Y/nMAt/7WAE78yflW+ef3t/lF+9n9XP5b/tT8iPo7/df90/3M/Z78JPph+7D7S/zJ+sf8ofu0/Fb8dPyC+9z6c/ug+TP5h/jg+An7A/xE/Zz8Hfwa/Jf9rgCS/0oAcgEFAbn+lQCcAWf/xwFIAqoBugERBK4EMQgtBoIF2gRKAv0DLQFnAjQAoP8C/6/+NwHxADIEiP94AcMCmgL6A+kCIAOVAC8CtAKWAcUEIAX3A6sDNAcMBgoGGwXsBLwEJAbZBp4GxgULBPUErQaqCKUHmggFCCgH9QNx/0f+T/tm/s7/LAPjAKb+uf10/Bf8//yc/vv7F/6i/0v+VAG3Av4CIwQpA4r/3fwz/6H9WP9DA/4E5ASrAlkBy/1//R7+4PtL/tf8c/7W//AAg/9K/Sn/df09AFsDJALkAoQCJAO1AMsBzP+5/uX+D/+C/pT/SADJ/lX+Vf8rAPQAcv+1/Rr6C/wT/JX7ffw5/Pz8Zvot/Wv8CP8E/lr9tP0g/ar7Mvy1/hf80f3sALv+QAG/Ag4BdwD2/nj8svx6/Mf85PyT++T9W/4o/1v/IP6F/RX/zP8ZAQsDnf+Z/i3+Rv0ZAa0CGQMaBBsD1wGUAI3/vP6y/1wBkQL3AOb/oAAAAdT/6gD0/zD/gQG+Ar4D8QWdBc0FzgPUAkP/eP4z//H/ZgLRARUBYwBj/1kBbQHtAeID0QJsAVoBcv4V/Qn+nv29/BoBQf+JAt4E7gT/BnMGFggvBOYCAQBu/vP+HAHhAgwAggEA/qz8PgGaAvsBZAMKAwgCuQHPAFH/ev/+/tL+LgESAc0A3//HAI0AY/5R/0IAVv+UAFMAb/7K/dH+7f14/0sCXAONAjAAJ/5E/Pv/qwCHAEP+v/2t+sn+DAEh/gICvADh/YT+tf4l+9j6kvyP9rT5UPoT/DD/7f4LASX+8P0F/RP9S/1d/Cj65fYc9O/zzPX8+Tb8ZP6yAjQECwPBAXn///yZ+/76v/o4/Rz+qf+2ABn/jf6m/Uv8VPvn+k79j/95APwCYwIi/z7+H/6C/Ev/KgDgAbYEJARwA6ABQABY/9QBEgC2/uEAGf1b/X/+6/25/Qf/zf8Y/jQBb/7L/tIBfQBMAn0BUwGuAZoBNgIyBWgDXQDcAHgAdQJmBT8H7gWaBG8DlgJFASEAfQFFA5YEhgNFBbYEhgMQBYgHGQStBbMF2AJFA7cCjgLeAT8ErAO9A2EDIQTtBJAFKQRkAWsAwABNAvMAnwHa/ygAyQMcBBEDSAKqALL/OwJEApoCvgSNA80DiQXCBBEFvQVOA8oCigRMAdUBXQAr/wn8Vf3O/tj+QgDJ//D+Jv1t/SX/Ov5J/y0Av/5l/Zj8Q/yw/MH9OvxT+yb8p/sz/R792ful+sP4xvge+ij99f84/o/+K/9C/cn8T/3L/GL7h/28+4L7ff5p/Xn/CAGmAa/+Wf9e/f/71P4E/dH9+f0D++r8Df42/kT8agG3/gj/I//6/moBqQLtAnkAKf4y/ez8gP4AAHcBnAMGBOQDmQHFAIcAhf6W/iAByQILAr8D//88//v/zf/3AJcA7gBEADkBZABU/lX9Mfwt/nz/TwA3AJH/RwCFAfcBHAAVAO79x/7FAQwBSf+u/6v9xwAPAUgAJAHE/0/7Z/me++75Kvyi/3H+I/02AMX+TP2Y/iD/1v3V/bv9Ofw//88AiQPmBYkFAQT4AfsAeACPAVIENQWUBzYGcgQSBgMGRgiHBiYGPgVoBCUFRwQJBIj/wP99/6AAqgLSAYADWAHcACb/MgHcAdYC4AXzAlkAtQEUAA3+ZP4//xD9kv7YARsAJwJqAXUAyAGSAbQAXv97AAn/xwBKAiACqgFbAT4CHQEfAwIBSADd/kz9MPzV/Qn/E/3f/03+c/2O/kL8H/3l+3T9Sf29+7v7pPvN/lj/OP/6/AD6L/oq+iD7Iftx+8v8zf2wAN8CsQIeAiEATf6v/HL8Z/6x/vb+jgA2ATEBGwEBAlgA7QDAAo4C6AO3AtoCYwAqAnkBXf+yAXX+vv8eARcCkADlACcCPgL1A8kE6AQZBGkC/gKWA50DnwLDAKIBAv9X/Tr+Qv7q/Rn9Dv9V/jT/MQGaAK7/uv5y/ir/kwD7BOgEDgVWAF//l/3+/Xn+VgHvAXUEwgREBFMDWAEnAgQAwf4+//MAm/8Q/yEC0wBgBFMDngCH/mv8ofsI+qn/2v4fAxAHIAUcBEQBxv7q/En+3P70/Tv+IP3r/UwAVwEeAGX/Iv2I+gr7L/1H/iwAHgHe/bz6lvqV+cX8nf8nAJn/KQH3Ad8BugOwAhEB1f+5/3MALABmAJz/Df+q/3IAGv/a/ZH8Xf0P/kX9iv2n/Lr9QP5O/gUA+/2g/jv+P/1N+if51viv97X6Sfvq/AYBLgFVAPj9af1r+xX8vv6U/9oA3wEGA2kCBgFIABQCfwIXAyEEdAOoAOAAdQEdAET/Yf6E/wUBTAM8BVMFowX+BZ0HKggPBsUFDgI8AWEB3AKmBFMC+AEq/Wb/af7e/9T/OAAYAPABMwSkAtICGQPzAXsAHwGD/4cAOANCA1cDcQPaAiEBqwDqAA7+m/2A/gsAYf8sASQD4ACnAy8DHwNzAm0AgP86/3ABef9s/mX+fvwA/TD+o//cAMoBiAHy/0MB0QFRATn/6f7b/RL9D/5H/x//NQEQBEEDUATTAFgBUwDuAGcAs/xd/Hz5RPpa/EEAbQFcAKX/Ivth+yL7uP11Ap4DRAX9A+EDGwDx/77/Ef+FANT//v42/zUBVgBl/z8BEv8sAOgCowBOAocARQJSAQ4BbgDBANoBXP4GAJb9yv6l/jUAZgGC/0kBjP9hAL4B9ALZAkQBiQAP/Cz82/wg/ukBAgIbAjcDSwIzAesBq/9zAUIBw//j/9r+d/7M/gkAof7q/R/98fx6/OL8TPzJ+Qv6iPsk/PL7WP1h/jf+6/7t/mP9tvyo+xf80PxW/Y/7zvwa/Bb7xv1R/s/+ef67/G77kvua/iABuwLLA3gCVgPIAbEAmv4o/qD/IACgAvoA2P9t/n7/8gERAR0C/gAGAW/9hf4JAlcErwTiBRADXwGk/wcCWgGUAdkDLAM6BKIEbQaEBdcG5QTtBLEHEAaOBwkJ0wesB78FXQTIA1QDUQMlASj+7fxf/b/+IgAcAfz+EgE4/9X/3wCO/xYAav3H/l//aQAZAGH+N/49/Mb9rPwN/Z/9Kf8mAYABywHw/bT8V/nl+Kj6vvtmACcDbwIvAWIAavxP+039cPyP/z4Anv/h/5X+4ftM+SX6wfxn/NH8Rv3M/Ab+xwHTAfP/fAAy/fr71fuY/3//Of9U/iv8EPtR/2sAgv9NAYn/vf/sAMX++f74/rj9Hv9tAS0BcgPnAhECjAFOAugCAwQ9A3oChgNQAqYDywEt/90AYgBlAbUBeAAV/6IAOgI3A6ADwgCfAJgCogA+ASoBWgBb/23/1f2E/VX+YAHgAxYGWwZ+BaEC5QFIApgAY/90/yj/AP+V/lD/rf18/2f/QgF8AToC7wJLA5YB+wCHAj4CFALVBKUDJgDxAPf/8P8hBGIFSgjuBm8BM/9p/RD92f9+AuIBFASjA4UArwGU+8H9A/1V+QH8SPyQ/Ir+3wGg/rsCVgK//Rn8APtg+vv5tfwi/X/+yf89AF3/Bf9a/bv7gPw3+6H7K/4t/Qb99v1J/r3+Wv64/VX/bf1x/WP9gv2j+/L8FPy3+8L9hP1h/Mn6q/jY+a77tfy//jn77/eA+BD6nvg3/i/9sP6aARn+I//L/nX9BP40/8X8HPwH/qL6ef78/wkAagAYA/kBLQEjATUAif/QATMDSQZ0BSgF7gB//4D+zwCDAd0ChQGxAc8DxwUwBxoGcQLAAtb+9wCAAWkFoAWlBoQG6QR1BHIBOgIYAggEJwVFBIoDlAEfAmYANALHAsEDYAR2BP0DVgXcBMMCTAD1/pH8IwExAzMBVQLjADf+aQBKAAEDRQasB9wFyQfPBNECuAP3BOcDfQIBAgcBNwAkAssDhwPHBKoEZAK0A+wAXP8//3r+LgEeAcf/4v9gATECbQILBFABQQBM/4b9/v2H/cH/fQGbAmoCMQK9Ap8BDgGIAVQAYv/9AAEBQgLOAXYBO/4++0v5r/ff+EL3VPox+wX8o/uB+1n8Lvrz+m/4Wvhg+kf7gPzW/dj9bvzq/Fv+nfzp/KL7v/oO/Zj+iP8r/uP8R/v0+sD7Nfnm+Gn4gvrD+yr6D/xx+xz9lACfAmUBm/6h/+/8V/+r/zH/9v1A/ev7z/ug/WX+KAE5/rL9KPvl/PX8pf4VAPn/YALiAOACwwLxAaADwQEcAYj/+gFU/f78RP4t/SEA1ADBAkgDjgPlAR0C4AJ1ArgEqQafA2UCUQG+/r/+QwG6AswDugQVBcQCPQNiAmIA/f5ZABsBJwJnA7IDQAMqA34B9wDbACgBxwGFAdD/6/7//ukALQJsBCYGFAYQBtcGMgbGBpAFKAXvANn9eP63/i4AcwBvA1MEoASdBO8AxABo/2kBGwSEA3cC0wCOAaj/qf4HAH7+wv4j/rD/9wBgAUUC4QFbAMr+uP9PAH//DwAZABH+yf4mAToCKgJOAg8BMv69/cP85/zi/J78Pv+k/Sz/o/zZ/NX5TfrY/bj9YQD9/VX+9/zf/rf/J/+WACQACAFqAuQDWwNFA2wB8gCZ/6v9I/2m/jQCpAIfA0oBSv7x/vX/vAOUAo8CywAD/oP8Lvm6+cb5SP5BADf/fP80/P34Vfd59yj4IPvy/QH9s/sx/Hz+Cv/R/4b///02/vn+NACn/7IAOv+Y/gj+Y/3FAMr9of3d/C7+BAC4/yECtgBmAjIBuQBMAHD/vwJaAh8D6AIQASD/Gf5EAFwAtwMrBAYF0ANQAmcAggESArsA4gDeAPn91gBSAUkBswKBAp4EFgMMBSsFVwUxBDQAnwJvALoCYwODA+QAsv6mALAADQS8BMwCLAEz/0oBUwGOBOADMgNJBNgCuAH1AQ0BcQBxAD4BbgFUAQIBQgDK/5f+Ff45/a/8Gv2z/UT/Vv+n/tv8XP3e/VH/i/9I/jP/E//M/ST9Dv/F/0cBgAIIAg8Bw//G/IL8mPqs+dX7YP0m/hz+C/+q/Uf9HP1h/PL9oP6l/QT/b/5i/Rn9ff5yAQsCqwRiApsBq/7p/B78HPou/Kb8uf31/d7+4QCPAIUAJv+9+9X4Qffj+GL6pfsf/5n+ef9x/az+if3j/78BGgBEAKH9pf6gAfIC6gLsAHn/2/54ADgBLP9e/+L9WP7y/k7/RwCa/40ABf8r/94AGwAJAjAANwL1AeQBRAOiAnsDEQFVAWX/Cv8nAFL/QADJ/8YCgwMsBTQFJgSyBOIDjAX2BO0EBASlBDwDLQL+A/gCRgOBBB0EVQRAA1kDtAI7AzsDYQJ/AnECiQI7AxkEcwT4BB8IagY4BYMDRAJC/zr+HAAN//D+OP5Y/Zf8SgE3AmgDMwZLBp8HzAjzBosEnQJ7Adb/WAFjAf4BQQJsAOn9qP5M/30COwP5AsMBsgEDA4gB7AAwAEP+hf46/gj+v/20/Eb9m/yC/XL5uvrS/oP/PAJ5AYT+i/n298D3UPjk+qb8Ef77/2T/Tf6w++z4Hfh49y73Fvjy+ZP5M/rt+eP42Pft9gj3D/fQ9y35jfo5+qj6W/4mAWEA1/8N/1b7lf0sABkCVAVTBaEF6wKa/1T9kfyB/qb9WwDV/ML+cf9P/6EDZgF+BGkDpgJ2Ak4CrAGx/xYCDf/YAW0DQAFH/kn+VgCcAAID1AMtAiIC7gHjAScCiwGJALABWwIFAgUEUwX7A0sEcAIvAmMC5QOEBdUELgVzAoQD4gCiAFwAIP8L/0H+cv0Z/Nv72fwf/qr/nwA8AKP9Cv+XAYr+NwHR/v/81fzp+kX8m/y3/rf99ADpAk8CxgPK/y7+pf1aAdsC5gR4BaMAPf7A/vD/+gDuAvMFEgS3AyIBL/8e/sIA0AFvBNQEcgRuBQgEaAJjAN3/eAGn/0cAqf8lAokAr/+X/z39qf1O/Dz/qf/XAQUDSwJaARH/TP9n/Sf+BP84/4X+L/+PAEYBTgOHAun+T/48/G78j/66/Qb9u/49/pz/iACx/13/5/4f/Yv98/zC/X392/4yAQ0BkgC2AXAAgf3o/67/9v5RATQAtgDK/iv9F/62/yAC7wCF/1/9f/1i/40ANgIdAXUA2QB0ADMBsAFWAoUChAHJAB8AmABYASkC+wN+BBAENQTnAckAFP+w/VL96/2H/1//iwHvAf4CtAGBAaABYf5uATEBVwCr/57+vf7I/+ICNwQBBqYEXwNgBJcD4wKyAqMCWwPEAToCwQFJAGgA2AC+AEz/tf8NAPz/3gBJ/x3+o/3W/9ABawJ8Avb/av+i/zcALwN6BAIEtgFp/1v8Vv4I/iX/TP5lAIAAPgHq/1n9//yT+9T+FQDi/ygC+v/B/YL7wvsj+XT8K/6h+5L/6f5J/oz9cPwz+o74uPhJ9+D3hfoJ/f3/ZAKQAU0AhACX/qr+SwDtAXYA4P9b/1D9zv6uAHkANwB8/9H8xfkD+/L6lvt1/LH8Ev4yAFUDTwThApEAtf5F/hUAbALMAYT/lPyc/AH8tPw5/Q39Hv8eAFoCYQPWAqYCfQEVAhcA6P8D/2j/rQAk/87/FgCHAKUABwFKAtsAQAHOAZQAWgCPAcIDKQXOA6sCRQIyAO/+0gHpA5sCnwTrBQIFVwVjB20F8QLXAh0B2f/rAekCegPsAuMCiwLnAXcDhAN7BhsFRgYhBeb/zgCsAeoBUf5aAOX+3QByBpQIKwcBBdECuQD+AAkBywEIAhwCFQNjA+cEpQSRAroCswK5ALEB1AJT/r0AOgEw/Rv9B/zM+gr65fzg+/f7vf4q/N76A/v++vH65vqx/Mj8MPyB+9j57fjz+uL9E/2t/Mz8Wvvo+4H+xv7KAN4AfgGIACH9Uf4j/Hz+KP3E+738qvzD/Zr+7gHz/UL9P/6F+hH80Pyx/jsAEQQsADz+yACu/FT/+f+a/6j9KwCt/Jr6vQJvAFkA6AUQBIUDvwIwAab/SP8UA4QCXAJ3AGMCegH1/hL/HP5g/9n+twE9ArMB7wIDAnEDAAT0BC0ESgORATb+b/8b/54B1wQABAMDMgOKAPz/GwFg/rkA8wA7AdMAOADd/hP9Uv7s/X79jfzT/nz/Hf9aADf+Rf77AGcCjgFhAkcD5gD7AgMD/ADrAikBVP9IAVsCXP4fAOcAEP78/6kBLwF2AHwBKQHrAOgDHQJHAbYCDAKtAuoCjwQdA5gDlAXeA3EDTAD2/pz9RQAtA7QEWQebA5cABvsX+BX7WQC5AuoBggERAf39rwDjAcoAJwA0//X+cv7F/4/95/zw+8H6kv0A/pD+QgF2Aq4DVgNTAwQBTv6s/qT8Df0T/tD/7P95/xkBvv53AHn9z/xy/MX5QPtD+DL5cflF++n7Jvx7/bH8qv7e+5n7/vvC/fT+HwAbAAr+qgChAFoBKQEYAOMB5AQEBioDFgN3AF/9rADfAfQBPgJKAdj/h/3lALQBjgKjAyYC1wJ5AaYDXwCCAXT/p/xDAG4ABwInAEUBpf5q/TEA0v6P/gb/g/1M/mT+3v1X/Wz9Ifxu/5QBfACvAVcBrv8aAN4CLQMQAyQEzwOOA70DbgPfASsBLgK8AJ7/m//n/gkAWgE4AYcBSwO3AakAK/6n+xT8NQBcA78CSQMiA/gAQf8n/s0A0v4SAUEA1P8VAXAA5QOxA6EEowM8A8ECfQGxAnv/+P4j/uX+OQAwAXADIgF9AL3+NP0X/VD+rwGpBPwFJgVuAjEBagPPAmICbQAO/Ib5IPpl/HIAhwK6AcYBJQGK/zoB4P8F/zn/Cv5z/Lz8tvx9/Bf9h/3Z/t7+v/7Y/Nb5mfrJ+fT7n/10/fb+0fzO/Yv7JfyV+mL6pfot+Yb7g/wUAJEBlwDRAPT8rv21/SP9N/xo/bv7BPtg+9/7U/w7+8T7kf58/x8AKQH4//f+Kv5g/UP8qfza/B7+Cv90/44ByAKtBGMDrgM6AXT9lPt1/Ov97/5VAeEAIgG6ALAC7AAsAG3/qwALBF4EFwYmBP8CYQPdAIkAt/+WAZcDyAX8BS0GoAMJAS4CQwERA7oEaAURAwsEWwO3AisEuwHvAsYCJQM+A/YDmAToADcA7f2G/QP/YgNABocFHgczA9QA1QEjA5gEPgW3Bc4DsQL5AnMCmgNbBtUFDgW8A3sAwwC8ABYCbwFdAFgA//7i/zsALAJFAuYDLwTUATUD1gEpBMUFnQaXBEIBZAEk/vv9Cf4E/2//2gBZAVoA6wDt/d38Vv4u/uH+vf5U/Cf8sfuw/CD8K/zI+9T7HPuY+Xb4vfdp+bv5Jvoh+iH5Y/sa/Ij+G//x/k7/If9s/pL8NP0++x/8j/zb+zv+/P+kAdz/y/+z/I79R/0z/c7/df1E/oj9d/08/977JP1mAIj5DP0K/zj7YfvX/0r67vX7/HX5gPyMAK//O/4B/7j9Ev+BAvYDGgLUARsAIvuG+if89vy1/9r+W/9z/7D/rADn/6wAFADw/UL8X/1e/YgAmwiDB1AJNAlHB80D8QEnArj+igEEAPX/Mf++/qgAlQMuBfsDCgZ3BY0DwgaNBToFqgb3BC8FvwMcAlAAWgB4AqkC6wXdBYEEvwJ3/9/+hvyH/Pb+iAP8BXYGAAbmAoMA5wC2ANECAwQSBR0FXASMA/AAeQHO/3z/4wFwARMBBgAF/xX9Lv77/eL/OgK2/kf+8f4pAFYBsgQdA2b/hgFX/xYApwDS/WD91/5E/8n/EwMaAPX+MgBi/qb+VwGl/5n+7/+N/iQAj/4p/4H/7f6i/qX/TQB0ALgBXAG2/qX/t/76+xD8RPon+xj9XwO5AtAEbQTV/NP9e/oJ+qH7Vfz0+r38NP87/5v/Rv72+zn5Z/o/+1D+5f8dAl8Clf5a/cP7AvwA/fv7yvrB+JD5Q/vV/jcAXQJpARABiwEuATYAgP+3/vH76Pww/Mv89P5x/qv+S/51/sP+pwCGAvwB0gKZAsIB1AFYAUABigCr/87/qf/YAEkCGgSBAw4DiwInAb0BygGUAsQCgQKYA4gDdQU9BhQHbAfoBAkE4gF4A1kFrgZtBncD5QMAAgcDUgXxBdcFAAW1B64EOgpMCoMF5f4g+Hf1VvkNA1cFLwerAQv6Xfdy+9YAiwWIBfT9ivYb9fX4Pv91A0ACGf5T+9/6d/91BK8FhgJx/TX7Vv67AFsDUAG/+ZbzJvM5+FP/UALTATn8yPe993P63/5s/cT7T/cm9fX3nfxCAhsBef+A+DD2Qffe/WoDhAH7/4/6X/lI+kH9uv/iAFAAvP9qAAIB5gCkABIAjv7g/gL/LgCnAvMDawTSAjECKwKsARMDdAJXBPIDJAMSAu7+TP+cAG0GfQYVBVcDlPyo/Rb/NgRFBa4EYgFh/oYAQwLRBBsCHQDi/LL8w/8sAs4E0wO0ApcCcwNfBhEGEQOI/qv5TPoA/rgDtQM9/2r4c/Qh+2YClgn0B0v+lff5+cgCMwsDDkcEVvvG9wwB5gpfDkMJ4f0i+c384weJDlcL/wB39YD37v+zDH8OYwXh/Kv3ovszA9IGVgOp/Lb8jP7BA5cFbQLd+w75cvv0AQ0GOQbNAxUCUwKpAq8CgP/J/OH+0gAlBBMD7wKx/ycBZgS0BgUGtAAD/mv8Y/+DAyUEdAFH+yP68/nY/W4A//0T+sn2JfYv/KUAtgIE/wb8rflF+0f/Tv9U/db3CPUN9x/78f/0/pf9/Pl+/JoAoQHqAFD7zPda+fL+qAMOAeH8KfgD+0j9MgIbANj7ifjX/JP/MwPhAZ78sPuf/1AGsQaaAa/5U/Iz9gb92QWSAiL/4/sb/TACWgXQAxP6WvV09xT/GASQAXH/AvpI+x8BzgSNAb39Pvyn/nsCNwXEAQD9TPxq//kEuwdcASn81/k5/a4BUAWdAmv+MAAqBA4G2wNjAHn+Kv9FBSgH5AWWAeL/PAFCBB8F6QIEAYwAngHfAsQC1gCb/zcDDAQeBLz/+f3p/p8DiQgTByMCbQDYAdADrwEL/Zz14fcZAdEI+wekASf9yPzNAdoHxgVs/eD4Pv3JA58GgQQK/rH6EAN7CooLDgQ8/H/6aQE5CCAGqPwx9or43ADBBMwCQPof98r6JgRDBnr/W/s/+5EFOgkzCAEALvoC/WwDrgbY/mP4FfoXAZYG9AVD/i/2gfrvBM4HHgLz/lP9zAPMC68LEQER95T49gDYBWoCaveb8Yr03wCzBaYBPfh89Q3/PgnpCeX+QPZy+SoBNgh3AVj1zO8Q+HkG/wkY/2X3M/gMAlEJSAPN9Jbtw/YPB48KxwG397723/ueBWoC5PqR+QD9DQjNB/79m/aH/P4I9AmVA5X5lfXC+RYH1QonAVf+wf9XBa0GXAEq+Lf2DQBGCJAJLQKL/b/9kAi0DrYHsf+o/z8ExwuSB9v/xvVy+BQLIBCXBxv/yP+vAUMJ1gzDACr4A/21CRUL5QCm+Kf2UwHzCd0Jrfz19kH+SQmtC2v/Uvf49a38SgcBB5P5fPRT/RUITgkU/qH23PaS/RYFpQRL+8f0pvhZA1YIGv919qv5pv/lAvoEEAFy+Uz7tAWuBl7+J/rU/akAngMQBEX9AvQQ+KYBawOYA4sAyfss/H786P+yABUAtf5fAOP+UPtI+ZL3k/aq++oAoQUNAVT7sPtBAl8D6v5e+wr5YfuGA3UHIP5g9Zn5VgIHBVkBqPun+4kCjQnXC54BM/w1/hsGJAlXBLr9d/ukALMGjAdf/3j5JP8SA20Cz//t/JP9Dv5iAfACmv/P/n8BNgKW+of49/6gBr4IWgQwBHEAnwPYBz0DjvzW+Mj+bQEHA0wBT/2a/rEBVgPrALoB/P7Q/7ID3wJj/Sn5Vv3zADcCiwJU/KX3bP6oBAEBOf+D/lX9jAIOBiIEAAE2ADgDGANP/1v67/jZ/QkCYgHm/ysBYf/ZAOADwgKT/xgCmAYRBp7/t/3oAJsB4QB3/Qr+P/9RAVkDXgE3/ov/qwG4/+UCVQVjBBwG/AW1BNoCZwMhAr8AgP/SAA8Baf2u/0MDhARsAtT7qPuh/1cEfgQ7/lz7sP8mBkcDwv0J+/n6cAEzBjkC9vkt+qj/iwNOAbH9b/n/+ef9bwE+/3D6+fmQ/IL+AP88/+77Ovl/+9b+jf+e+yj7e/2K/ZP/gwGt/6P7X/1w//P+PP5T/Cf6wvwSANkC9gPy+1z6XAO3BOj+G/u6+zz7JgNBA2H8t/iB/MkEzwAW/Hf5wvuLA8kFd/3u9W78KQDCADv/Bvs8+/wBVgf0BAYCQgLgBFAHrAWsAUj/bf7o/6EEEQTtABf/Ff+o//kC5gAb/uz9kAAFCJAKaQeyAJT/LwIXBpoH+AKg/VL8pv6UAAgA7ADZ/7n+gwHnAzoC4v05//3/ZgNKAib+EADc/74CBgVDBdMEUgq/DagENgEK/6n9JP97BNEE1wH8BL0ENwIGAqoCcgRiApYA+/6dAGoBggKFADL/3gB2Bu0HSgJk/dn6Wf64A4sAEv9g/tMBSAJ6AOj8jfmI+/b6Wvu2+n8ALgThAYL9NPoK+WL56/4K//j6cvmV/e4D1wXfA57//fsc+uL7F/2k9m/zQPmB/DX7qPrh9fX2Sf5mAAb/Xv/7/If7l/6T/kr7u/vk/JP7P/rm+fv41voOAJkCFwH/ASgAXgDNATYECgeSA6cA6gFdBN0DSwFXANH9/f7iAXsCQgHE/yoCJAS2A44C4AJHAzoBT//W/oj/mAKvBEwChgHyARoEGwXnA34CXALDBPkB8gHFAq8COQERAYgAZfzj/u8C+QJp/rn5JPxaAU0ECwOK/038XPw/AhgFQQD5/OL8A/5c/XL/RgCL/Zn9yP4T/4T+agGSA2z/Ff9r/4sB9QMIA4z/Nfzs/ngBHAM4AKD7XfzY/1QCqQE7AXj9wv3a/b8BNAF6AeH/sv7T/i7/JwDP/T//4ABXATYEnQAa/XH8aP9+/Cz+xgL/AmcE9wHs/XP8jP+0AdgBrwDQ/QkB/ga9BQYCdP83+vL4jPzz/a//5gHQAqz/A/87ALIAmQIgBBEBl/77ATMGnQSbAKb7YvwMA7UIXwaF/Vb4h/sLASICUP2f+JH68wFiCSYIKf6R+Nr65wCBArL9Xvmh+Fj9ZAFYAV0AKAHTBREH+wZbAC37g/4nA6YCbP1C+oP5pftzAeAAsfvH+f7/LwVKAyb83fkr+6L+cgI/Auf6OfnvAbcGDAQBAZb/OwAyBK0E3PyV9k75GgL1CJcFK/5l+Rj94wMDBs4AwfjY+48BuAatBNz/sPkC+gcANQFKAAL+uf0YAEkEFwVsA0oB0AAbA/YErQTiAbMCtwMwBGIDZgCM/qUB0AauB00EiwOuAaIDjgTZAwQB1AChAu8CNQQBBBYB2QFhAi4B/f9D/v399f9MAnEBOP88/5ECPAW0BPkC2gHOADMB8QBq/+X/AgE0BFgDzf8x/3P/8QPNAkIBQP4I/XD+4gGRA8j/Ff7c/MH8vv78/Vv+Pv76ABgAXADD/uL6P/qq+jH7qPhV9hrxOvLR+Fn5TfiB+WP5cP4zAeb+zPja9Yn6PP5pADP8F/wa/Mb+WQSmBQYDGP8wAO/95Ps6+4z4dPhK/F4BWwH/AMoAIv/6ATcEdgWUAyYCBAEgBPQDoQJtAhACUAShBe4E2wHL/38A5QEEA/0BqAAXAVUE6wRiBbMEBgToA6sGbwRB/z/9XP5w/9MBBANIAhYCSgMhAxYC/QBO/jYBZwSgBHQEgQQsA0YCVgKVAVQAYgIpA44EJwXYA6QDrAJvAUv+xABNA0MDpgTXAQgAgP3K/8oAg/8r/8H9I/9M/bj+4QCwAQcAQwFxAk8A0P2N/lj8kf0cAM0Agf4i/dv/iADIAvYAb/5r/wkBKAQjBQkCR/5v/BL9uP2Y/zj83/i1+eT9mQKeBLsD/gBp/tgBMwIdAZz9w/uO/Dr+rgF4Afn+cPq5+Wj7f/wn/lL8zfva+jr7avxq+j75wfa3+AL66fyN/yYAi/83/mH+n/5A/xX9Jfr5+Tf7u//jAUIBdv/2/yn++v6BAK0BlgBe/Yj9ZvzI/VoAUwDS/fn+t/9//33/RwEI/tX8hfyu/Yb+DgDTAVT/KQRdB/EIxQaYAKv7Nvsg/uj/nQHX/6X+DAFWAyUC8AEiAZP/NQHdApAEIwJIAbv+Tv1t/6oB4AKCAfMAPABz/w3/Pf6c/zf/CwF5AjMBmQCCATsBsP83/w7+GP/pATkFagVtBKYCZwIYAhIC8P8j/UL8e/9JAZ4CNAGfAZj/lgLXA7MFQgPsAaX/hQBTAUsDjgLj/hL9fv/3AoIDLwJq/2H7O/52AmMGNQbjAxIDsgHVAcL/IAKIA/UE9gZLBKABsv6aA2IFGwDXARr/vgF3AxkGBgFQAXD/b/01AEYAYP6Q/Xv/Vf+U/58Ak//d/CP9jP3D/TEAdQHAAgYE4gHvASkD2wIb/hj6j/am97D4+Puk/Vj83vqX/CcA2QBXAMj/C/2U/O/7F/sc+dj46/s7/Vb/tfvI+PPz1fQy+Mb7q/2d+/38c/zr/SoA2P/R/x0Azf86/bv90vwH/kj/5gBW/7L/1P8kASYBUgB+/q0AjAKzA7//d/4Z/Df+4AA2A2QCswEjAqcCIQLq/o36R/3H+kz+rv4MAHX+kP89AA7/Xv+q/lj/zQDSA9wEKQOgAygA6P8g/77/6wAhBCcFQQY8BhwHAAboBk0E+QL1/2QBegH2AH0DBwFZ/1j/MP2iAMMEmwZeBX4GsgIvA/IFEQlPCk0KVArBB/cECwQdAiMCBgSyBEMD9gMTAP79AP8ZAfMFqQaKBIcC+v8uAdcC7gV6BC4ECQKBACQA4v+HASgDUAMgA9cCsQLNAHEAgQAW/wz/yQA9/w4AFgC9AZ8AEwBG/5n+Jf9z/jIACwEvAuACpgKoAq7/lP2M+V/5Gfv7+9z92P57/V7/Jv+9/u372PrO9/L1K/ig+f36/fp/+i/69/jz+HX2iPhs+R/8xPua+WH69fqH/csABgAB/tP7Ivxk+V39/ftI/IP7L/sM+ZT5F/yw/1YDEADX++z3XPmm/eQDJggHBtQEqQGzAE79ev2T/ij++v6RAH8BFv+xAhwFLgauBpEDogKlATAB6f+U/+L9Sv1pAMwB0gAZAjkC4gCeAXICTAHoAEcCcAK1AaECUgBa/f79Nv5u/5gBIwHv/5UBZwHvANAA3P+C/zAAygEKAS0B2AL9BegF2QaaAtL9AP0a/6kAcAQzBckFWgJ9Ae0AcP9i/vP+cv/b/1YA0v+w/2YBlQAiAXgBTv+S/wsBTQFL/l/9z/yY/SECaQMPA7IBhP+yAGgB+wAtAMz/4v3X/rf/GAFAAHsCgQHH/vv9Bv27/9QCmAUYBLUBAQBRATEG2wXNBkoCDABD/K786fyS+wz9mfkm+kH5Ffsv+9H+WwH8ASABWP8B/un+SwBt/kD8u/f+9YH4Lf7rAqsEMwMi/lT61fsMATgFQgZyA5v9yvno+lP89QFlApYDef9V/PH+sAAaBCYElgFD/PL71/8JAgEEugJqApH/EQEAAb8AqwNzBmwG6AVvAicA6QEaBFMGLQi8/dz5zvZ9/d8EdAh1BlX9Gvfy9B76xP7vAacFxP8C/Rf9Q/8PA3oHjwVo/+r6Zvr//EUEKQdlAcb/Tvh9+C7/xQTrBEEGOQJt/hwArAJ1B1gFrALj+4X6YPsQAMMHCgcEBUT/pPwZ/VIDgwiFB9UGuwGi/1UAcwOpBvgEfQOJ/LX7XgCIBHkGbAOY/sj67vyWA2YIsweKAnL7pPac9i/7Xf0c/2/9oPkX+Qv8Qv7l/6z9pfpB+cX7N/0NAkIESAEN/nv9SP1kAIAC7QEf/2z76vYd+lD7Nf0c/PT5EPXM9Kf4+fl9/ZP63fap9gP4Z/mZ/CX+JPr69yP4efqg/2sE5wO3AjL9LPyB+8D+WADSAef/+/wT/KH+zQJYBgcGrQGm/xr9jf9UAsIDSQKbAMD+0/3YAnMCQAWZAysBiP7T/lkBIgQHB0kFTwJXAEsBaAQoB7kGRgPh/qP82/x9ARYGlggLB5b/Q/wn/vAE/w1uDc0JSwCe+4T9/QQfDBgJRgVK/Lz6/QBzChsOgwZC/sT2DvufAwgOtQ3FBCP7ivm6/6kHTg/rCQ78p/UW+E3/7Ad1CggEpflD94X9/gMYCOQE8/yh9On4EwJmCroLwwQV/J73yfx8A0cKNwmTAWL8lf6J/y8DFgW/ANH6qPlA/Cf/rwWmB0kFqgDY/g0B9gYLCuMHGAEE/cn8agIYBr0FRgDi/R/7hv/mACb/zfrk+1L8sQCbAdT/Nff/9xb5wP5BA7P/d/lP9DH2e/0WAq4Am/f88/LzfvsoAv4AoPSX7UftaPolB08N8gIK9FHtMvXGAMYFrwDU88XtIPZjApkHbgD781bvQvi/BZILgwfb+Jnv7/ZaAooIGwZi//P1Y/ib/zwG6QUeAnD9+f4d/4oBwwE7/hn8Yvz5/08DVQQjBbsC7QXtA98GUQJkAzgEpQXkBs0F8gO6AvoEhwhHBRQDNP2n+wX+5wcYCbgHSwDB/nX/KQX3CWEIIQJuAJoF2QpECr0ElP4v/KL+lgajBRUB7/ZR+ZL9xwcNCVADzfq9+U/+/gSHBwb+Q/aj+E4A5wmlCtsCufnj+w8FVgxrB9b6v/OJ+LIBnwrUBG/4i/Kh/bwJzwzHB5X7b/WA/WoGAQYx+wv1tfVk/60JqwcR/xD36visATwKhgif/dP1GPrpBNgLdwhN/CjyKfYPA/IKTwQn+rrzP/rmBGQJnATF+Xb2a/r+AtAB6f2Z9G/z6/zDBqwGYv6/+Kv3zv6LBicEpvyw97X6rQMoBi4AFfli9vb7jwRfBQEBNfs7+x7/JgUsA0L8Aff5/OQFswZKAzb+GftGAfoIDgn9/3b6/PtuAgcHdwXn/Yf5Qv7tBbYHoQRkAMH/hAS5ChQJkABj91f1RP5HB+8IPwKm/Xb+2wLQCfgH6AFu9n/34v8OA3j/hPsq+T/9bQdoC1ACU/Yt9MD7BAMuBt7/D/RS8qn9tgMVA0H8qPiH+iAFHQmPAxr7EfjJ/KwFoAruACz3DPtVA7gHzAaaAsn6Rf71BjQJ///99mX31gDrBtUGyv4a9/f5pQJgCDMDDfmG+D0AygjnCPwAjvWg94UG7Q8jCm38aPlsAmMLOglUAGT5igC9DvgQ6gKQ8XT0BQL7C6EFYfjz8PT6TAwMEMwDgvJl8RcCMgtmB/r8ovil+bwEVgrtAXH04vJa+dX/8vzr9VzvmPO+ADMKawfB/vL5/v+XBpUGYP6O9c/zqvtzAeb9Cfi/+XsBTwfG/6D4y/KU9/v/4f9R+9v3Nvft/9gK2AKL97z9hAOkCSoDov4Q/KAB6QdjCNz/rfwvAfgF/gbZAlP83/6QBtEF+fzN97H5egPODWwLz/84+i8BnQj2B7MCifsY+I8CtAszBkv8lPq/AM4HiAyvBif7RPhRAnEIMQdRBMX6BPvhA/wGX/0u+0X+ugIZCDAHvP9b/AEDQwfiAGb5hP+IApUA5AEeAAP8WAQrClECHP7P+4/95v45/m78SftC/SwDcwLf/RsAx/1v/eMDCATuA3sGFAPg/psE7wHF+6b+OwLRBVgCYPr69Yz6DwTAAr389PhO+iv9oAEdAb/14/RM+UX9f/78/fH8KP4aASgBWP3Y+Zv6jP+iAJ78TPmQ98D+ZgRF/0H6bPpN/Ob/SACA+gH4wAHpBvcBC/8i/xYEUAk5C6QExAB+A9AICQppAkUC3QVhBhYK3AS3+zz7QgWTAuP9dvlV+csAyQWyBIr/9vzr/04D/AE3/x78k/06BI4I0gajA27+Tvyd/2oE9wKD/H77Zv46AiMEzwHv/YL8zwEwArT9ufow/K4ATwBIAmwAxv/4/3EBYwHR/LP9bABZBuoAJf31/E36fAGgCBADhP5H/tf+wATgBLv50fUD/Sv+UADgAB/9zfzXBBEISgL//xcBlwHxAjAByPm0+AX+qgQRBrkDV/4j/pYD/wd4BIsBt/5M/9wBZwQHAt79aP59/dj+HQIKAokBZwbLC4wFaQM//2n5cP69AQT+0vey+6f+hgVPBLD9NwHvAU4Arv/R+czza/m2Am0BNAAcAhgB3wHKAs//ZPxZ/QUB7QHSAYj91/mx/hcCBP58+Wf5Qft5ARwErf42+Nv4mf7tAywBPPn09Rn4Nv44A/oC8P+K+lT5B/yE/tcDeQbmAsv8s/pN/DT/zwGs/iX32vUk+X7/OQE2/3v5BfWP+Tb/QwMKA4L8dPj2+2EFGgohCRQCePp4/rUEAgiBCNwAoPtz/2UFpgRwB6AELPw0ASACYQBqAiUI+QTtAm4FaQLDA1QJ/wlEBjwE3ARRBzIJwwVPAoMBXABUAiMG0v97+G/4gP6fAI0CSwLZ/tn8RgEhBRcGIwPOAY/7jPsu/4IC+gAZ/sb7yP5WBBEHBgUHATn9A//PBLEDvgAAABr7NPmu/gkEjwCYAY/+E/oNAZwDWAD0+wr5QPML+DQCDgNcBZkBAfyY/FoBJgPdAnwCuv1d/AUA3gAyAHv/yv33/WIBUwICAmD/m/22/QMAlAGx/qX7qfqk/hcBEgIVAbL7+f2sAYEDnAImBJEEigFbAr/7Yfen+er/pgJ+ACD9ZvzmApUFLgUGAq79lv3lAsYHZwNQ/sv7n/tW/7IAlf2E+b74U/tE/UH/ivsM+2X+WQAZ/n/68Pvr/gsD/gOj/rP5//mTAUEFXgB8+yv8sQBmBFkHoQDv+Fr6rwIxCPQE2AFd/cb+0gKyBa8BIPtn/B0ETATxAWUA6vu7/d0EQge9APX/6/zK/LgBLwRQAicB8gAH/9kBJQLw/vAB+QP8AioGqQjqA3kCfgXNBc8EOATNAnL+GAFrBBwEfQPFA/oCdQBaA2MBnwJRBIYDMAMnAxQEqQBOABIBX/9wALf+Sf9ZAKIAkgAEAnkBQvwh+ln8pP5aADD/H/+v/AT+WACoAP/+f/7Q/MT8Yvtl+/f6Evpa/D77dPp/+j38nP1Q/U/87Pcq+Fn4svs+/uH7bPz4/Jj/Jfy9/C/6mfgI/B78u/og+0f+qPz4/LP7Dfnt/JIAEQECAS0ACADEABkCcQJgALn+5vwy/xz/zQA+AVUCFwKoAt0CkQA+ANMBtwJyA+ABxwJwAFUA3QBbAv0AYgDkBJ0H/gioBdIE4wOQBCsETAKkAXn+BAE/AyMEZgPQA/IEIAQqAW4CJwCC/4QE8QVPAOz9/v7V/UcAwAA5/47/GwC5/aP+v/0n+3T8bgCk/pj9jP5e/dH9vQBiAAwA0/9UALD/SP99ANwCqQWuBLP+gvqT+kL/SgLGBZ0EGAH//14BvQVfBEEEQv+x/AT7m/yk/9T+nPxn/JL/KwOGBZQGKgNkASAD1QR7B60G4QWgAqEAW/7F/Sn/sQBlAlYEBAS0BO8CeAIeAbsAof87/7P+2v65/K3+DQA7A8YDlASSAmoA3wFVAe8CiAAl/4j/3ABEAxoCdgL6AGL/nv2c/Uj+7vtR/SX7bvpR+F36/vpO/Ab9VPz9/VwA6gENBtwE5gFt/Yj+Lv1Y/4r9rfkh+FL4tvm7+rD7nvw7+0P89v00/cr9Gfxo+5f32/rd+bH8X/6y/rv8XfvG+yL7gf4fABwADwC5AcYC4gNQAar8uPuU/AX+sADaANb+Uv5r/zUA1QCA/cj7vPk8+mz9XQMOBMYAiwCM/6sB7QO4A9MBQ/7B/2gALgKjAsABMgAXAUECaQJRBBAFVwNzAnMBwgGZBAQGLgX5BGcDSwOgAgACogDY/rcBsgJ4BusF8QReAWgAbgKLA94EIATwAjgBIQKoA4QEZgQzA94Auv/8ANQAlAKKAQUCDQGDAf0B8AHsAQkB1QC0AUD/RQFzALwDHATSBBYEpAIdA68AHQK5/Vr+DgBJALAC6AABAPb8uvz1+Ub9d/+hAZ4AbwC8+9L7x/ls+4H6uvuc+jr8+vyZ/e3+KgGCAGb/NP3p/Zf81/4J/Wn9f/2C/VP+q/1h/2H/PQMFAo0BSQCL/sv/Q/+gAJ7+CP5d/C38bfxU+/b+xP8AAP//qP47/TT9R/4sBK8BqAUABUkEdgMWBQAFsALtAbz8Xfx0/VX8Qf3W//P/cQBCAHX/zP35/mv/aQGpAgsDw/8GADn/tAHkAZAAlP3b+jr7kvu0+kD55Pdr+bn5O//G/xECsAK9Ax8CqP7S/9X+yQAUAB8BCwBz/kj+6f5oAFYBRwHUAIr9bf27/FH+OQBYAH0AAwAQ/6v/TwAVAtgAygCWAMMBXgOyAEQAx/9KAAgB+QHlAGz+2/6cAHcCzANAAuYBxwFWBOQF+gUjBkkErQVgCAYKgQkEBy8FrAJIAmgCSgPYAcz/uf8g/oP9APz9+9P6Pfxo/37/awCt/Qb79voY+w394v4HAEgATgNMAr4AEAG6AO/+zP8WAVcBUgM3AtcC3P+t/fb7Vvvl/RYA0wGhAOv+O/+q/1cAzv7i/cj+dADaA/8CKATTAfMArgFr//P/1ACNAfsAqwKPAb4BPAIlAuwAjQBIAakAkwHnAGQB+P9l//j/O//p/7P+s/yA+337ufth/K38wvya/7AA8gLOAvsAuf5x/S/9DP22/UT7F/uU/Ez9y/4c/3H9Q/xz/Ef9If7e/xgAbQDH/47+vPwc+/z5qPvq/Iv+kf5h/iL9Af5h/xAAGP8X/TD8YPub/boBWAPWAv7/of7R/tQAnQOeAhQDtQBI//3/JgNDBoMG5Af/BcsCHgKpAn0CHwDYBOgEGwcLB+kC7P/eAF0AOQJgB1wFUQPVAgsBlP9NAlwD7wIVAnQBKQFoAQgBfQEvATj/qvuj+eX45/r4/QYAmgG9Afj+zv7z/RT86fy3/cv8rvyk+qP4ZvfE94D3bPge+TX4PfqZ+UD6aPq++pH7P/p//P35NPgm+Mb8rv3L/voBaf4kAbACoACOATgENwKZA5EEfv+N/T0A5f+sAHkCpP6l/TwAogAXACn/A/6v/NH9sf06/GL+/P5UAGwAR/4z/Fn++wNeBIgHswUtAVoDWgM1BRAGrgZxAygD5QKaAtgDlQMmBi0F6AOKASIBtAPoB40LxQrdBQwB7P0PAzgHmQdwBwQBaP03AVwIUAoHCvUDxfkU/iQC0AScCwkKUwL7AQMDdwT9CdML8AqvBiMEIwOtBV4JPAmbBwAG5wS1AkEBnQDC/xYD5AFJAtgDmAAgA0QERwJBAVUAdP+GAbMExgKjAMP7O/UN+Cb+QQFgBDEB3vjX9Mv0G/gS/OX/s/z+9yb3ifmAATkE1wJl/cf4Ivcg+Sb/jP5K/6f8rvrc+5z9SP4t/o3+zfsz+5z8Kfhi9jP42Phm+on75fpZ94n3Afow/ML/k/vA9z31dvcc/Z8CaQEt+2f4Tfec/FYEfwIt/+H40PWQ+poABwHP/Y74v/Ju9xP/qAQ+BB7+/vOM8573J/4EBJ4BCPy39S34/vzQAXsEQQCn+qn0bvoqAf8HTgleAob68fUE+pkBowzzCzAC9fuW9vv7ywjkD/4JmQB898P1ff8SC38MRAb9/Kz21/0dC+MQWQxRAH72Mfb2A6cO3w4yCDb9t/ig/xcMhg8uCXr+N/Wj+KoDhg2iDe8GUf78/IUG8hBtEyoMewJl+1P+NAi6DVENpwT2/Tj9QgXLDKMNewet/m35nfyUBPsJ4wa/Alv9Bv59Ax8J+ghHA037K/mh+67+ef+z/HH3fvfE/uEE/QZiBcH7LPn6+34E3wcKBV781PUV+yAFdw5hCxL9mfGa8Fb9aQdlCz38c+xL6Sv1EArED+IEp++b55PxrwVrEgEHjvFi453povx1DbYHMvTN5THrLf4VETATyfxO6pPoc/kyDakQQgKT7MfoRPS8B7MQ3wXh9QPtNfiYCOgPEgc+9knv5/KKA28MdQdD/DT1x/i2AmwKFgd//j75gfp9AIcFCQeyAs0AW/8cAu4E3AMJAG79W/2Z/M4B3AEzAE/9gfnG/Ov/bghqCdMEgv7O+sD9OQRuCkQG8f5X+w/8jwXHCg4IFwA8+7T7EATzCX0FcPuP+Ab7mAMYC6EKmALSAMkC1QgWD7YIgwTYABcDJQnvC9EJngIbBAkEKQgzC2QH6QFf/rwANAMwCaYJrQQrBD4D2QT6A1IE5gC5/zQDUAQeBt0FkwHgANgCpgO7A50CBQC9/j7/wv6m/gH9EvsV/L7+agGpAMv+Jv7D/Z7+GACPAU0BwwGjAqECeAFV/5P/6ABCAGD+PPz9+KT3WPzC/Pj7H/tw+N358v1C/5n8bvox96/3D/6qAGf+APw1+Iz3XvsC/UT8+/mr+bn5R/2s/cL88vuJ+sH5d/l1+E/2D/Zq+CD61vzh/cL8wP31/f3/MgFl/9b+iP7j/7P/dAHoALoBWwOOBFIGfwR7AnYAKv0y/uD+EQAn/9f8pftw+vf8aP59/nv9vfw2/iT+oAEqAvMCBgSAA/YEmwLNAywDVAHeAX8AZQF1/1r/HP5h/q/+rv/JAQgAwwA0AEEBTQLRAjwEFQHZ/gQBBQQFBAUFxwQfAR4CGwMYAsMB+f8hAHYAEAJmAjAB8wGZAfYCSwWtBRwDnwLwA4QCXgMeA2ICNQKzA44EQQO2AusDuQK9A/4DhQFN/zv/rv/9AR8EAAIzAsYDqgSZBwMHcgRxAlcCnwGtAoQDdwL5AqQD6gRBBKoERgX8AMv/9/55/f7/bgOOBPYDlQLc/lf7rvoc+IX+1AZsCCgFJ/1p9CbzZfnT/mj9Qfhe89v1lgDeBzIHVf0y87LxyPkUA/AElQCn94v2Mvl7/lX+JPeP8lfyyPRZ+Sf9f/w3+jP/if+t/or/4gDX/o4Bmv4w+8r+FgLoBcUGoABn+Vz7nv4KANQCdfoo+z78Kf4UAVb+qP6t/w8EswLlAZz8KPiH/cf8qf38/WX9L/wgAOIEwAHQAa0AHQAyArkDNQKhAV0ClgC/A6kFzATOBA0ENALOAZcBpgLaAhUCkQL7AncC6/4d/Yf6uPzpACQEmgM6/cL5QvqY/ZQCLgYPBEUBFwJ4/1sC2QUjAqYDrwRtAzgDYwIz/yz9iwBZ/oAC3gSkArADzwLPAoACuge2BGEBTgHG+zH9HgCSAs4B4wAjAtoBfwThAoYC/gDrAboCqAPUAnMA0P9B/wMBogBRARkE2gM1BTIFlQUHAcT/OwAV/6EC7QCvALYAXAJQAs0E9AMe/5sA1f9u/0gBRQGu/B/8Df7q/An/dv9n/XL/dALVAxgFPwJ//Bn+Yf2K/s7/QP/6/TP+mv9t/g//Jf1a/KT+tgBsBAcDHAKH/mD8cv2v/Sb+Qf1z/EH8l/wL/fL9vf4B/+r/vv8z/3L/MP9z/Zf9qf1s/WT+Rf2W+yr6rPm2+iT7M/3H/In88Pwv/OD8KP5g/uD+nv43/9f8JP3R+6/6XvvG+Wr6xfr2/Nf87vux+2v4cPva/Ob9x/tP++T6pfouAAwD1QKbAHz9P/zO+wX+K/68/uL9MPzn/mwCnAMjAqz/ZP3d++EBawVWBLYBjPyg+n3+cwSNBskCZP0N+TH8cwJtBjIGjgJ4//P/bQQZCIYH+gBe/Xn6Zf+iBRUHAQLI+/P7Vv8pDJIQWAqVBiQBXAPFB84MzQc6AzgAOv3uBdQHkwbDArQA9P/vAywKKQdUA2cAcf9NBQsMrAsLB0ABvfxk/f4ByQYABogCMwDZ/j0BNAQtBuEF1QXCA8oBnQFdAO/+Rf+j/7IAPgCXAnQC+AKBASAAfv2l/tf/NgNPA6n/Jv4U/aP8Sfv19+D3u/cP/4oB/ADq+qv3yfz4A/MKPAak/RT2nfctA4EKzQmy+i/vcu+D+aMDdQPz+HXtE/FsAM8OdAzE+ozqUOrL+h8ReBXyARzt4+rD+7MRbhreBuPs+uVM94IPehmECfHv1ebC9zMQAhsQDNnxx+fH9cYPshrFDcbzneal8vMGeRTjCTL12ueh7/kEABM/D579bfDt8ukBfQ3lCsf8wO+K8jz/+gqFCaP/vvRt9nAD9gqYChv+HvME77/3XQLwBOoBb/mi9yr8xAHHA7oBQvuh9fX3B/7XAsgE8gGH+oP2ovpGAHsEUQQkAMb5evpnAJIGmAqFBtYB7/5qAREI+wdPBZ/7vvhk+7QDMAdYAev76vfk/pwH+wk/BGP6Pvna/nUMEg7WBjP9RfiV/e8FMQq1AR/6ufaX/BUJEQsaBJn7a/f5/SQGxQjgAF33ffaU/K8HQQdKAqP63flGAT8KGQzEAqb9TvwbAnYJFQnCAub6rvwpAIUFsgVQAPD8Tf0uAiAEfwRkAAwApQEABOMFBQJCAOT/6AJnBWYFnQJk/+7/twEXAoMBxQDA/nf9M/5K/qH8NABxA04C5v/a/ZH6CvsX/4oAIP/c/p7+KP9sAUcAJ/9o/+b+MwCKAWgB7P5n/jgAgQGjA8MBO/46/LH8z//R/9oAIf3w/W4CFgU0BWkAw/1U+jf98wA7AKf/Sv2L/5UAiwHRACgACQDaAS4HOQfSAG39sfqq++L+lQKSAggBCgQNBIUEkwMTALsAdwM/B1IHVQV7AXb+WwBUAHT/CPwP+dP5wPyPAHkAgv8WACEAaAPJAr//Kv3d/Ob+gf4p/3X8ZP0y/hEAFQAs+/L46/dE/Kb+9PxJ+v32vfle/An/Q/uN9yf3nvnb/Qr7FPSb8Ln2KgFmCiINPQK49zv1cPoh/48Dbf9X+ez5iP4LBT0F9ARuAwoBegKGBL8D4AC8/oX+7Ptw/mP99frv+uD/RwfZCLQK3wFN/pb/IQL4BeUC6//6+9/8o/0ZAH0DcgQPBusFoQKv/hT/ygSZDEAPvw6wCH0AQ/5VA+AIgAjABZ8AYPzi/KX/mgGIA50JVgvkCbkDC/sS9p78PAXfCMsItAMnAIcBJQa6CL0IHAi+BbMDTv8o/Fv9NgE/BVUEqP2K9k/1d/uvBKcJVge5/1P7QPpp/uEC8QTnBA4Ca/85/AT8Wf2O/0oDgQLWAFj+0/zI/bH9pv+7/Vb8evk8+oz+GAD2AfkAhP/M+on5uvsB+J38KQCUA9gEpQVuAT79vPqQ97f7AgBuAHv9GvtF+Nb4P/wj/ET5XPib+Tj98wCSAdwAoQCR/or8ZfxU/Hb9AwDQAV8Cf/9I/fH96wDSBXsKVwzqB0MBrfzb+jP9FQE5AwoC2f17/OL6I/yk/Pf8of39/AwAFAE1Atb/LgKXAvkAugNTAuL/x/1T/Pf7JQEvBdwEwwFB/Ij35/jD/YX/0P4a/Jn5VvqD/QIAGABKAn8CkAHnAUcBLgIQAqMBCf+x/LD8V/5MBJ0DfQGc/ur5Vfm++6ICSwVSCOcERP80/Cn97wH2A2kGYAMF/7L8aP3nAKsF7wkiCmkG7AI7/8YA1AT9Ba4EIgGQ/SD8KQEWA/YCIQO3/rL+xQEeAlIDJQeqBNQBuwGBAKgBpwMtBvwBr/7Y/MP8ff+2AXoD+wKvAwwCFwC2Ae7/tQJpAvoAjwKwAJz9R/tl+9T8eANxCiQK6Ab+AAf+Ff4D/3QBCQQmAwYCJgFh//P7Evsj/vz+PwHVAOv9Evw1/U8CiwMSA439Kfq0+u/8IgTWBMICcv1y+2D7LP/YBN0DXQHC/fH7f/8EAjYBOf11+Iv0yvQ0+5P96f0Z+3j3nPdU++oBQAQPA4P9VvqT+Sj7uf8hAGv9oPyX+sP76P6z/3f8G/oB+Vf7wwH0A1YCAP63+Sz3LPlD+LH6y/sE/Wf+AAB6ATIBdwQ+BtwGeAO1/+D95vhY/YwC2AW4A2EAEfvQ9ib8h/9zB2UH1AD3/AL+6QFdCKsN3Qbh/GL4dfhu/lYHPApHA8L8J/mq/hQKGRIBD/QDbvtp+Lb/4QkKDJYGK/3n9nT1U/9hBxILiwf3AWX9lP+lBtMKDwlDAwX9Bv6GBdIOuQ5cCxQDi/9tAGwEhQZPAjr9C/ke+nD+xQHNAq0Aw/4f+6X6w/uX+oT7avyC/Cv7yvyM/lYAAwE4/y38QPmE9w35/Ps9/L78zPvy+An55fom/7wA2wMwAOD79fkg/JcC5AbRBk7/zvcz9sz7RgeLCegHV/0W95P3mgAaCu8IbAGs9PXxNvoMB8gM9gSS997tgPVPBZwRdQ7a/wbxvPK+AWIQoxOGBp/0+u3k+f0KHRG+CTn4IPD99iYJEhNeC9n5Nuur73sBAhIOEW8Am/FR7kb9ew3LD3UBi/GK7v/6wgy1EsUHuvW87lb4CwhUEaYHVffy7Vr1JQU5D8AHjvaz7jzzRwTvDm4JCfkZ7N3yvQKdEQQO1v157QTtdv12DL0PRgME8tDu7fkQC1sP5gZy9ivx5/qsCe4OXAYE9jHuUPbiBl8ObQv0/FT1jfr8BwUR9AmW+zDy5fbNBDwPXQ+i/0X29/YLA20NjQ7SBPz5UvpaAjEJJAnB/6T4lfcbAtwHIAnsAUX6Xfn4AIUHewgIA+H9WP1dA1UHYAZD/hz4QfjY/2AGPghJAZ/7bfoFAPQFBwdyAbf8oPti//YEzwcnAlz+wf1lApwG5gdeAyD+Nv3HAacIFwkwBIP+9vly/X7/LAAK/qn9/v3rAWYGOgHM+vH4uPzfAHADMwFZ+8P6OAIXCgIJ4fw78pXwTf2KCzAPkAKK82PxVf2bC/ULZ/277U/uPv0pDBQNsf5u8270cAJuC/oFP/TY6hHxpQEwDJkGN/lm9M78YQyoEN8Gv/ee8f72RgBkA7v8TfZ093QAnglrCCgBqvW69Mj6MQL8A2v+0PuG+7QBcgWUAjT8wfVg+f7+eQbgBVwC+/uf+hcBXQVBBjgBS/3s/Or/mwOTAfT8Q/pGAXYGuQihBnz/1/vYAF8IpgrkAw76IfOP9YH83v8z/FT30/fI/lwHrghrAaP7QPpq/5oDKAT4/VT76wERDfUNoQau/Kz5rgBXCyoMKgL9+f778gS+CncGHP55+a0A5QrXDiIIrf5X+08BYAjtCCQCYvsb/fgEJwkzCFMDVAHBBbAMAgzXAu/7D/xQA4EJ0QX7/hT8ogITC2sLwwD694L5AQPfBzgDvvjK9Xr+vgn/CzAB7Pb092QBwgfKAu736/LM+uIEfAfK/uH1AvX0/FYEMwNN/Hn4afs2AoUC3/yU9xf3Q/uOAd3/Hvq89+j6swCSAr79evic9wb7UgA3/2z47vd3+qICdgS6/iv4tfds/W8DnAM0/Oj4qPpk/moBIP2K+977OQPEBm4D+v3r+wr+cAWMCfgFof+G/PL/jQQVBIsA3vwl/pQBQQXgAVD+iP45BM4HywVL/L34z/vyAA4HQAMr/F38OP7AA8ID2/1++0IBWgW5B1kCXfmb+I398gFDBHD+N/lx+iv+2/3Y/Xb+bANRBvgEof8Q+1j8RQIWBHEB4vsC/rEEfwhbBfQAVvzz/GkBpv8L+wz7M/t+AUIEhAJTAe394/5VBP0ERgd0BOj/tv9I/0cB4gMRAmEDagI6AUT/Wvoz+4ABLwIxAEv8MfcU/aQCLgQZBef8IPmLAFICiwPgBZ7+3f6jBGUB4wOmAlD7+gKrArX+JwNx+nX8gQM7/70FYgIw/CUDYf2E/5sGEgCPBVUEBfuHBi0CQv5YCU79yv2wBG/3twAqA4n75gf1AEr9oQHB+3MCoP6TADEEef9YBaYBaf+g/wwAyQAJ/2AF+gOoAWoCVf779lz8TwXH+F0C+wdP7+IFkQvn8uUMDgIV7XMHdfnx8lYJkPtp/j4HGvHv9/n2J/ZbCtn6hfqo/dX1kAbPCogDEwNGBGIAOAO0CJ3/JfxvAKz6XfzCB/wCCAKBCbL95vwFBKP92AEoBRf+7fih+UH3N/uXAscBoQFw+yIBIglZ9u4L9wki63wMJwjP3QsHVxec2mkOQR+VwacLpixqzT4liR/1sz8f+xKBzQIsIxRmy/8PTA5P1xANUxwg64D/LBLi7I/0uBto9ygBZwzI5kH/Afw3A7UTJ+aKCbXyFuJZLU32bP3HHZ3lNAA5DbED+/9D+GgQbedf+r0m99l9A4kgYc+4Bjkci93tCS0ePO2B804LMPk152kUWxPm1BgRBh1XzyEhcSQqzU4XTxlM1MwIORxV4Zj76STt6tHsxB5S6RTqHiYo+lHjOxvIAG3p0yPqAz7liw38A33ySgfjDbbwSffdF2/yOfbxG9X2j/9rF8n4m/KBBi8EmfyIDXcJsu13ABoSpPlDClwQy+jF/VsUA/nJCpMPoPLB90kJpwCr/3QS3QFZ9UAHVAMz+GMSoA9s+J8HqQPo+IIKuA+wAez3LPwd/ub8rhOwFBP1BQQaCHLwrw7rFSb0ZgNGAyjtVv+SAeb8WwPT8cgD8/M478AcM/G98noOB9j/+EcbCuq5+6MLsN6A/PgULvDE9uD+DvA77lUFrgat5Cz9Owuo4NUGmRej4DcDbhVv5ET9sxJk6EjsPBJX7kDs5xcK+Sjx0xhx+uHsCheO8iHzURg85+z9xxLv5RoNmBCG2jYSBg5U3EQbbwkL3LQQFg/p5bgMpRDy7VT7sxhA+wTw3BRI+svtQQRcDzP3//JoI6f7ouvTJOkBruGAGmoJ89zvDbIIB++O/JYQegqj5BIUghHR2xQO6xUt4u8OZRQt72gFpAeP+w7/hAFkAnULnvj/BZUXDOp9Ahkkmev39N0rYOYM5T8xCujw3ts0ZPUf3XwxawLD1Pwj2BYF2KAQwx0B354BMChv69TzyR6D+OnywRrSBL3tVAo0DE/2xgQ6Cpb7zf9qCTUG/ADJAf0B/AS6/HP/QQct+N4ApAwrASv//QOqAP77sAJZBzL8KvvOB/cBavzmBQwDBvcHBNQF3vl6/g0C8/5m/VsFYgLg+eoCSAc9/ggAGAQC+I73XwIv+nbzJ/1i+4H3gf0IANzznfYfAFv3HPxT/3n6BPui/+f+jvtk/MT6bP1C+uH+8QA8+k/8fP2s+bb3Sv12/in7MP52A2P+BgBcCxMCBP5fBjYCpvwdA1YAwfg7+xoHYgOQ9tsCI/4D8lYCkA3V8L/1RBLn9JH1uhGW9ezmHhj7BsXugxEt+rvp4gpNCebxwP3G+w38NwdpCfb/lPLYAIcIFf+tAbz8uuZi/BwTa/YI+TUIh+8oAOIcZPw27fcELgWUBIsQZBBz6g7rIRvr/ybzoxLG8eHqUSEUDFjuXAq89/P9vR7cB/DuYgAR/SQAHRw6AXPnjP+DDjf9RRW3CnfgDgD2EUj/S/07Chj1PPxcGdoKJPLT90oFhPtEBlUOhvfr9fkNww7i/D4C6gJA95oBNxmG/DvyDBO+/FL7sRiuAKnwZA2FB3T7mgygAKz2vAO+B9z/gv2mAt/6Ef4yDT7/MvY4B/z7IPqiCFL7h/ebAD8NZf1S/S8I7Pbe+HEUNQoN7rkQtAJk6VcYiA3L5cALEBJA6jwSOwyd4xL9iQqS7yEE1g7l5+8LQAtV9gMFJgL463f4phIt58/4CwzB7OXwOhkg8zPhYxrz8mvq1h8M/j3rSBdD//nutQEI+xrxsPcdAnH7EfN08hUH8fBt8kANEfBT8uwLEP3J8McFrPx16z8ITfn996YF4fph+oUEIv8T904J2v/n87cBlQ5A8KD40R0i7errXh+a+Frg6CFfASLSDB0rCxnmqhJ0GJbrYfvkD0f0/v9GAWcReQSt9u8VkwUr63sNEhNX7CsC1RUR9l72thWMA8rwCQj6EVH4RvbSHpEEKOmoG/kL8N0HC1sbD+wg/gcihfg07MoZhw1A6CAKlhuF6/r2GyA+9FrmuR2HBhfp3hhxGEju6v5RGtT3cfB2FLMEYeyXA2wTwfa+9PAYaQG253YJ8w567HT1bRwi+3frdB7jCU3bDwUwFx3kO/PQJlXqeOYZILACyed5B8MVjeeIAvEW7v069HEJ+w0C7kYFkwlC83X57g62AX37TAX2/VoA8AHCAb8Ai/3n8ioIIgLT8Ff8w/4F8Or10w5A+k7vZAUKBgPwf/syFMLxNPBYFX4B5+clBs4QgOgT908Z0/mh4R4S9Asw2JgAox1U69DpHSjJ+jnVDRLlF7vfQ/JOJ6PrMdikHccQJtU5+ScpHuS34yEtlQfq2JQSwSO830Hw9SLQ+4/gGRA2FJPmSPWCFs7+peT4CMsS3fNb81MRlA2u4N3/oh3r85/nQxV0FPLXVgZwHDzsWuYcEHsSht8wAy8ZVfmY6CIOzBvS4q/5QBj5+szniwgkEZryYO1/CS8T1fIO7AokTgleza4SmSxk4i3gLT+hBFy9KyNUM6jRJ+cTUAMDBbrZKY0twsCD66tSBvfRtk40oDBvuoHz+lLr6uK2YD9rL9Gw+AnsUhTTGMuISxoar7FbGxA8R8+o3XBCcgZtwU0iVCk61N7yBiV2/2rnzQXjEoD61OHVAbUjB+sK5IEvMAUwxr4cuiZKzWsEKDBi4a/i+DG7+FLdaib2CjbXaxRhG1/d9QrCF/Lff/atIfnxRPJ3IeX2G+QWDaILyueaA7MX4viM8jYK5wR46ub8ag8c9uTrWQPGAuvxe/swEhb6qeg8Ai0Ku/HE9OET0vob5/YJbBSw9c/4SxgZAy3oSAktEJLwU/gbEfT63t9z/V0ENPhmABkK4P9A5sD9sghGCJz/j/+J+5TdZ/pbDIsME//aAqnuhuSLB1oOLAyJBdjyod5K7CQGLAzLDXoAJu+E74X6KQ6JGD4AK+eH9PD+8vfOHlIlBPFJ4nMDkAQB+9wZThT96s/iowGNHJkM3QGyB4P7WeAU/J4tMQZR2nT8PBEr3eABeiht923M0AlQCVgDywZ8Bef5lfAg740XOCeo483oYRu09c7lUSrgFczLLvW0F4b7ZgDdE+D2xOdw+P0HfRnNDwTvvfgIA6/xFgNZH3gCh+sLAhgDeQTJBmsG5/+O/+/4GQWYIHj6ZfXsA2oJIux/CkciPfiO+OcMTAqR87EIFRC8+/4C9wc9B+kGKAJ6910Gage/9zgCFg7p8jD03RK5DVb8Rv41CMz7zv86E1gR2wLL9QoA+RJsC6r7Ngs6/nvqYv+NIRkEK/SNANb9iPcNCSgTlAV88O/22v0HBswE1vv/AD/5oO53/h4SxPmT5gQCE/tu8CwCQwgl83Pps/pOBMIE4/sM/Kv90PXr84ETfQqR6ZjvOAFz9cfw9BGf+kjs+PhQAeUFmPzn9x/pXf5F7XD2iA+O/crlz/jnCNj1/vljD7kDc+xY/1cLlgZO9ZgD8//68931bAZwENf3w/meBV0FBPX7/CQRyfnf8P0K0g3B/PP3iwz2/Z73mvinAZkAau39+q8QAQg26XAKfxTa9drwqhtSBbjfXgZAFMP0sPT7Dyj/TfJ6B5r63g3Y/ozwnAd7DerxpPxFIc367PMoDB4JMfOX/qYLcQL19EX5SAWnBI76FAUME5b83OvHCpYQW/FJ+okb+v3U8o4aeQvN8RX9TwfJ/b4CcQjo/PADsP7F+v8KOA339On6rhqy/6T4SxP7BoroQAe3FxP7SAIMCdz63wERCPoE/QEMBc/xkANPE/v4CP9NCp8AtPRWDh0M7/dS/UQB9AWfAewBkwR0/e/zFvvjDAv/YvUiAOYGKfXY/yoNJv9p8t39MAkb+fn9wQLl+sz3CwCVA/T/BP/K9R34kwMI81z/+QfF/7PyggVHA9zy/f+2/Qb7RfW7AY7+uQPI/B7/KgHA/Rf9Tf6hBVf6WPcPAKcEhvc1/FMCBvj+88MAHAi0ABL/gAb7BRn7x/zLA/37LvTU/qb+i/159u8BWQay+2z6RgK+AL/6FgSPBT/9i/bC+cMFDQIh+QgKCAmo9AsAfgkz+f73bgpaAJIAewU5AWkCGwrc/jABiAtB/EX0wAvbDpLrYgDlCkbv5fYXFo4I6vM+Dbn/cwCXAEv/wAXcA7IBiQVkEtr1/f0ZCdkCq/zQA9D98PtNASf7egnQBmb6CgJjFTX64PvFD1cErfppCbATX/nkBCMKSgKh+ef9qANDANsFuQPD/6IAiQA6CeQJSwt5/p//IAumB9v91ATlCCj2UwOZFiYF1PfCD8wG/fwGC8UHaPTn+BEAg/jm/y7/2vdr8cv8yfHC9HYCf/9O8/8GoA4I+uEF8Qy/+Bn2TAdS/Lr23f/J+pD7YACb++f7rwFk8937TwaJ/Un3dgBv+Tnvwvbz/C/7e/hF+6v4h/fa8zT9bfve+3b5zP+d/Vz1i/YB93H+P/1ZB+8Ewf39+u39gf6m/Wn/Df85AC/9nv5VArMI//+yBlYDzv6M+VwESAP4+jQCIgWnA74Duwo1BVH9xACLBZYD3QL4BV8DjgG6ADUBkf0O/u/8YwKHA5b++wImBUEElgDWAyoBkPvkABwCkwAt/2H/MAAh/28A2vzr/sf6WPwhAIP/Iv66/dAAJP9/ARABCgJdBB0DhAH7BOoFIAClBXYFR/2c+gwC0/6M/OMBdwMm/ob+/QR8/9v7JP+oAeQDhAjtCeIBQQA7/fr9Bf6NBHr/TQTQAngAFfsSAun/vf+kAy4BrP+mANYCD//w/in7W/w+/+AC8QQ0BSYCZ/zzAOT/JQGJ/sABAQBOAK8D9gKT/tL9RwELAB0BIf4J/cb6CP4u/x8EKwMfAGQD9ABI/1P9G/8C+4X6DPos93T23vjC/F38X/8Q/i3/Qf/oAVf95/ol/T77B/1j/98C1Prl/ur/yP6k/HX+GAAz/yT+twCJAVD8Rv/dAhEDfv3QA4AAhQAh+wgEcQPxBZUAIQiyAAP+mP0yAssClAGuB4cDZwSg+u4EOAN7/JMA9AHg/C77ngMi/eYALAM2Atb+vgPk/mT94gH+AnkDhAi5CVcC2QL7/e3+ef5IAF8EzQT5ASkCNgABABoCSQUBAqkFNv5RAqkBzgNO/5gCkfv9+ST9j/7k+Xn7m/0G+RQCHAKhBBz+NQXF/1sBfgDg/mP6KPrX/l/3iQD3/mz+bv7hAlD75/u/AOr+c/2L/yIBa/3Z/6sB9QE3/2X/UADc/jP+gQDXBWMDFAJjAhn+1vxy/SMAev2GA40B4AIyAeX/a/YY/fD5Tvwj/t8DCgD/+7cAcP0f/Xr7VAL4/V4D1wVAB+cDcAH4AE7+dgDo/8sEhQKsAur/8wXtA1YDpQImAED6vvpj+/P4SP/8/mn9DwQB/q768wFcBtD/xgN6BdYAj/0iBI8FAwH2AgoHMQIb/8YAuf6PACAEAAJJBT0AP/1//uMCeAdeB7kIlgjBAt/+SALvApYATQQaCeADOAKT/0cBKf+E/3MC8AN8AYr/EQGy/kj6HvxDAbv9JQAHAbIBhP4pAbUClgAtAXD7I/6J/rD9RfuMAav/rfbZ+A354/OQ9ysCjAHy/KX6wv3C+KT5Rv+uA1sAn/+aBDIBSf3k+yT9avs4+c/4NfpD+PH1wfxHBWkACgD1A/cAnP9x/80HnQTMAYz9AQTN+8H08f5LA1776fw2B1YBXP6DADkH/f9JAGIFdAXo//P/EwWRAmoBNAF6AH0Cg/1z/nQC7AMq+l8DPQZcAGACvAZdArH8pvkU+W76l/c0/1UEagM//WAF3wXo/vf9RQGo/q74igF7CBcDnAKIBEv6ivfJ+4/+swQmBU0ByP6uAML6QACbBKIECv2V/sr+sP3b+wAF1gGF/hD/fgDC/0oBewLEBNoAi/1K/u7/WgDD//7/JwBB+6/8cgN9BTb+mAENANT7nf8QBWMGegL8/8H9Efqa+ef8sQPqAVr9VP0C+jr2Z/y/AwYDQwCQ/Ub7LPq8/eIA/AOHAMT8UP7l+7r9cQBKAcYC2//m/yj/hAFp+qH+DP5J/fX9zf+dAx4BpQBjAW8DK/5fAbQBhf6u/4QDxgalBuMAMv1WAjL/zQTYCpcI/wAyAB4BegOlB0UKoQwUBDX+1/8nAEMAywd8BnoCevob+Tz8CwDUAWoDhANL+1L4Tvxw/Gr/2f/j/+r74PbW+0IFgwN+AX7/SfZi88X7jwNaCWAFPv0T+sf3tvi6BxIJuAXB/Oj0PPWy9+v/lQd1BG/5wfj5/Av/JQj8CR0E0f7K+MP8xgMnBF4CswGD90n2UP4aBqcD8AQm/Cn0Ufbd+0gEbwnwA4n6cvwA+G4CjgoZCt0DFPxZ+Sz5PQZAC/QM3QSo/AP7ZP/bCRAMfAh4AOf2PfinAm0LsglXBLb5DvD5+IP/Ugc/Bs/8lPoa/bECEA9oEG4HGf1m9kH2T/3zBBsJ0Ab9/LX7z/1K//8FVwc//wf7M/ug++8DeAdSAK365vMh8XX7OgVxBjEIVwHg+LD8AgEwBTAKZQJ/+nX5UvvB/p8IYAcE+4j3uPZH+DwCmwk0Bor+8vlH+0QBaQgWCE0DfPen8Af2uQBWC9cK3wWS+gD0t/ZD/Z7/Rv1q+cX5ofmi/fcCwQK5AkP+U/9Z/Mb84vfU+0X9jfq+/kT9gvnu9tD5HP6Q/z8A8/6u+qv6d/72Bq0HIwFl+EP1svpvACQH5QLo9dLwvfXkBzoLmglK/dftueml90sM6g0TAlr2qe/v9esEQxG+B1j1Fusu8wAAUw29EMgDcfOw8VP+1QwnFXkNSgA/+3j7UQb2EGILrv0k9zf75QAnB7AMXQVv/Lz+IQimCqIKzAwrBksDRAFiAtj88vsLAD4DCQdLBugATAJhBOQFQQviCYEEWADcBHcHuQejArj+0fx4AIgF5gs1BoL+ev1CBMAF0QetBpQAXfnuACkIPAmrBpQBJ/wIAGAEagi7AvH8KPYf+24CPALy/2/6z/e9/WEEBweI/y/6mfjj/QcF2gQD/vX4EfRd/KsEMwimBPz+n/vz/N8A5f9R/AH4bveM/P4BdAM2/dj6U/zb/lABdQAs/AP4ivr1AyYFIwDM/qb+zP+wBNUEn//h+I78lARLC7YFTv4w+uf39QDoCJkFWvsR9Nb7wQOLCvQGFP+89tj6cQSNB7YAy/RZ8WX6HwXeCQsDI/jx8un51AXyC1gDZvpw9ej8EwZyCKQCE/mF9W/+PQpvCXH+ivUG9Qf+IwkRCir8BvY09fUALQwZC1b+EPhu/N8ERg54CYv77O499e8BZQVQA9/1+O5T+TwJdhMoDEf99/a6//8LrBAZDHP+VPgiAxcK5Qj0/R34A/YyAMkLegf++9T1r/eqBZ4QVw5EAHD7wPr8AV4LAgzL/lj31fojApkGJAYN/y74F/wABAgH9gO9+dP3Jv3gBL8Fpv8h/Wr9wQP/BtYFDf599jz3F/30Ah8B6P93/Db7iADxBOgBrPpo92T8nQLLBoICQPzo+Ln5ngA7Bsz+YPeu9lb81/8OA94C1/2X+lv+RQRJAkT8efjV+F7+9wAfAd/8HvhJ/DADVgV3Asz+TPzJ/eoCAwUGAyz99fp+/HgAgQH4/Jf5uvek+If7of4p/h35WfvH/tEAvf58/hT++v0bAzUF/AH7+jj4Xv5CA9cBT/+R/B78yQTfBVEAXPnt+o8CcwRRAxkAVPp7+8kGBAxTBdz9x/pD/IABtwVyAiH3gvVj/6kGtAwsBML8jvynBL0ILQYo/+X2zPx/BgQItAMn/QH7XwLDB58Egv/9/vUG9gqOCrcE7/ww/hEJEQs9AsP4X/o0B20LJAGN+B330QC5DKkNnAFC/Nj+/AqODhMCnfUG+GkDAwf/Al38t/qIA5wM6Qmq/v35TAHLCUsJDgDT+XX9BwLzAJAAEgHcALv9oPw1+2T62fv9ANcByf7R//3/yf5K/lYAkwWYCAoFgABy/E39lQC3A9f/G/oV+JL9zQGFAQP9gfdg/aQDuvwr+0r55vma+qX7JffE+V/+PQA4/lj4Cvm8/3MF5P/P99H4cf+gAt0CG/169pr7WQKS/7v5y/XQ+O8A1QYZBggAa/yKAJ4GuAYdA/cAJgLUBzkJkQMuAE4ALAAZA74AyfxE+wD/mwWVAvn8Ef4mAuwF4wID/vn6z/vP/DUEQQTY/SH+ff3I//wCWgNC/pP8hf6YApEFaATl/x//IgK6A3EDoP/P/Jb7Dv+NATwBxv/N/Vn+GwKTAxcCDgHQAuYBDAEfAX0AeATiA+EEXwRc/g3+ugFWBpoDsgAT/Cn9XwEzAvz/QPuJAU0G8QaZA9b+APvD/mgHTQbL/rP94QCLBV4G9v/I/LIAPQP//iv+Xvxy/H4EHgNPAFEBLwWACkoKVwFL+ub8If/XAVwAAgAY/8sBsAWIAwwBkgK0A0cBmf8f/A/8EAISAAIAzv0z/E0AJgK+APL+/P2Y/Oz91/8c/d372/ld+FL3evc6+Pz4s/l1+ur41vmO/ab+p/zH+3r+4AH4AfkAav4r/fz9bgD7/wH/xAA8ApQD8//a/Dr9TP+q/bb4//cV+en+vQJhAZ7/5/3X/UMCCAE++5n3v/q4AKIEcASK/zj8i/kK/LD+tf5AAB7+4vqP+Xb+lQORBjYGkv/b+sP6Mv7wAQABUf+E/eX/kQD3BnQGyQJA/9P8a/sf/uT/Xv5Z/UP6+v3CA90GFQgcBKsAEP/d//D+qgUGCOwFRAUo/2X9hQEBCQsH4QWKAqkAoAYaBxUJZghaAs38JwGJBbYHVgotCtgDlAECArYDMQhvCd8FAwJl/XD/iQY+C4MI0gCR/94C0AacB4oCa/oa+An/nwMNBsYFBQRnAtcCxQVtCCEINwPi/ov/iAIiBJQE4wDM+5v7Nv2x/yf+t/xO/a38Gv3r/Gb71/zX/jv+Nfvo+8L6v/wGAXL+aPkU+Kv6H/2g/Nj6tPdV9OrzM/nU/Jz6dvp3/54AbASWA5f/t/o0+RP7/f0U/+j85PmP+uz7owA3Aev+I/u7+pT7jv0m/y/7QvoW+Ij7z/6A/+T+Tf6t//b9Sv8M/XP7W/0+ALMASAR/BbMCLgE/AfP/tPyc/tj9IP5NAkMDswC1/SP/sgPLBqIHygKU///9rgJcBaECN/9v/EQAwwW8CHEF8ACVAKYCgArACor/nfcr+A8AHwfHBoj/DvuC/qgFbQhvBJD+G/6ZA1EHawaXAFX9zfo6/c0BfQErATT8mP35/p4BJAX3BSAFigMMBKkDtgCe/k78Yvzi+2YAUgH1/mv9KP/X/xcCUAIRAOf9MP7A/X4AKwQGBTgEAQR9BTAEoAX3AZoAo//B+7D/sQBGAqUALgIk/0b/8wOwAi8Awf38/Vb/MwKUAqoA7gI0AloDQgINAC3/Uf9/AdkAuwAR/iL7Lv0HAGb/SP8Z/57+Kv4P/rr/rf8P/sv8ef54/1b+pPvZ+i78JPy1+7j7Xfor+vT9qgJFArL+RfzJ+qn51Pvg/5z/Afyp+6L80vv5/qQEngScBO0Cbf4W+1P5Dfrh+zn83vj/95X7Cf6iAbcDHf2o+av7vgGyA9X/9vcl97D8SgFNA/0A8P17/iAD7wRFAnz9uvuw+u38EgCAAzkHqgStBHUC7ANyBNYGBgadAcn/ZgACBcUHhwgECFsESAQsBFsCLALF/23/Yv4N/2sA1v9iAdsBygV2BqwGqQf3A0sBI/zC/jsAIgJ5AqH+uABaAOcCbgLpAaABWQFcAc3/jQGxAMAC0wET/638p/5L/9/7sv3HAPABiAOaAx/+vfvt/hsAjP+gAUL/tP6A/3//kQDrAQoFyQLDAUv+B/lJ+ZL7Gf6s/FsA3f47AUEE+AY/CHoIgQfWBlYBd/4M+of6Kftf/gkBHgAm/f/+yQCtAvADwwJ5/mH/JQDZ/0gANQFEAfIFfQgPBYL/wv23/F//8P///V35nfgj+qT7qv9OAa8BtQOIAQ39Rfkj/Iz+awC8/yv88vlo/eb+QAB6/37+WgC3/7r//vyF/rD+vf67/7X8r/ti/AT/Hf4//vf+iv4xAA//if9B/40APwIFAs8B7/6g/Nr7hP/3Ak4FtgXZB7QHnQcnB0QElAI1AEsAegA8ASUBAgP5BL0E3QVfApf/nwB5Af0EfgLk/wv7NP17/4UCqwIRAWX/2v1dAWUD7QIOAcv8+/xp+xT9AP1V/vj/ngCzAPL+WwDn/mMARwG7AWH/h/uE+678R/3F/PX8/vv+/eMBPQEbAYn+GAHPATcAAfxr93H25/hr/a7/cP15/K78WgCwAkEAaf5v/isBUQK1Acv/nf3w/v8AtgM7AscCLwLLA3ADJQPmAqkDhwGG/T3/sf1OAiUErwOyA44B+AO8BCcFjQJJ/338u/qY+rX7af9rAHT/yv2S/JP+tAFvA9MCcwAO/BH8wfxj/AkAzAFcAi8BLAF7/4UAwv8V/p77lPp3+/H+NgJgBN0BlP6B+rv5f/p6//4AowAU/Ab5G/kV/Nz8Mf5h+gz4r/kF/X0CGwfQB8gIGQQuAgMB5gF4BY4HOwQ7+wr5AvhY/4EHkgg0BLL6EPyp/2MG4gwdCNMCNf5v/dH/EQuICy0HmAE8+vv3ef9zBVQILAVp/1b5t/jn/zMGxQnEBX78G/Xq9gv+3AZsCRACgvjK8wv20QGqCowKaQO6+hbz3PZSAUgGuQTX/q/0M/R6+rQCuwOi/3L5R/R1+Ij+jgNPAc39M/n2+Yv9TP/iA2sCyPxT9wz2mvkFAVcI+gYPATf6jfeJ+/UDZAbMA737Rvb09HL78gRgCWgGgAAU+Nb36v3LBOcG8AK8+fX1vPl/AHgILghNAV77ffRD+m0DNwyNC1ADQfnb8VD2nQAfChQKowbK/cX3G/5dBakJHAVt++T01fcxAogM+g2CBwf8Yvf4+1oFOwncCeACKfkj+yr+AALXBjkCSf5U/Xz9GAHHCQUM7go7BmUBm/5h/44GbAfAAkn+k/un/NMF4gs2CQkGKvxe+DQACwiKEE4MDgKW+gX6ogEqCqoO7gjg/3H7sPvtA3IL6A5lCmUAafnL+OL/+gXjBTAB5vic9jH9ZQd+D8YLtwFx9bPvM/hwBeoPcwwNA+v3MfWl/LkHXA2SAyb3muxW703+DgmmCwIEPPUj77P1zAKjCXMI4vwe8fHyF/5LC44Pjwj1+dLtfO41990E4wf/AI34UfS0/CcIwwyBCQ392fLt8Tb9fQPgBYP+0fTA9Ir8gwU5CbgEufnG9Zf3wQJTC4ENBwRv/GD6P/wdAkUAoP1A+D326fzOARoEEwM2Ap77XPx+/0/+S//5/JL9nv7I/uT7xfsZ/XP++gJSBT7+gfeA9VP3av9CB58FnPtL8ln0Zv0TC/oOhQUO+aD0jPiWBMMLHAf0++j06fVVAKsIuggn/0r4KPg2AKsJBw28A+76Efdl+mkCWQdCA4H9qPkwAOwGiAi/BHv7qfSr98wBVQmBB/j/f/ew+PP+AAaDCXgDVPxn+uj9p/8JAWEAZ/yF+7z+EwKEA+D/RfyL+vX60P6HAr0CuQC5/Jr8uvvtAXQFXQSv/kD6YfqRAS4LNw53B2j+2PgHAaMJehIdCzH91/HA9iMHLhKREWgDpPQg9JwCeRB3D74DdfJ47+z8EQ9cEtcIAvRi7Sj22Qc8EfUIN/ag6+3zhAc8E04Rh/6t72bvTwAHEEwRogM29Lfxn/0yD94TdQXS+XrwsvVRAiAH4P/89bLx7vgTA4EJeQRr9yTwevjRAioMFAiM+5nx2/P1/osLJQlm+wXylO3s91gG9AjVAnb2xvSw+bYEtQabAkL4zfOs+0gFWAfjA8D4XvSB+aAGFQqiBWD7G/dq/SkL2hDHD98AyPhD/GUH/Q0REPoC0vfQ+ggDmwxDDPQCmPsd/E4GWgtQC7L+FvqD/J0Hqw4wDFEA9vZO+kMEDgq8CEP+jPgn94YDfwhxBJz9EvqS/WcHAgwuCEL9HPgG/0oIXwrLBeb4SPKG9tgCYwckBC35D/fg+vICagdUBJf66/nsAJEFUgNW/tPzQ/TQ/V0H1gaf/+74kP23A7QGgAWZ+qfyNfzNBMEHBgB/9qzw3PhPA8EGLACk9VDz9/wBBuEGsP7y9vf3zQPQDZQMrgK895T1Af60BtoJXwPq/ZH+uQTFBq0BKfhR81z3cAI3BjoA3fit9tr/7wbDCBj/WfYh8Df77wMwA/P99/gN+lsDyQjyBLT5p/P0+bwEiQjRAzn6rfPa98YCMAcAAyb6KPh++RYBPQd9AlX6p/Uj/DUDRQVXAl35KPWJ+DQDAwbqAXT82vku/EEAbAXyAN75IPiV/F0BWgE//xz7Ffq5/ioI6ga+ACMAOAFfA5kIdwcfAhT/QQLhBRoFcwFN/l/9gAGoB+UHlQKeAB0A1wXOBmUGrf93/ID9lgMUCVoKjARxAtsCPwVaCg0KzgOE/0cBygOFBXQDFgCz/ocDAQsaDf4FVgBJ/jcAHwTVBTEBpf14AIgJlA0yB/7+T/lJ+goFrQzHBkj82for/60EsQUs/If0afbrAh8MdgZQ+WHy5/dCAuEEefxu8SPwFf5JCQsIHP2u9ID5ngNUB8/8f+8R6lz3pgI0AvD2Vu9o8sv/lAiYBOb3GPSj+nYE3AFH+nb0jPfW/rwHWgAb8wLxBfmfAMoDxP4D+Ln4+ACTBscBOvac9lb8AwOqBDf9VfUG9/cArwVkAfv4H/lIAPIF3wZU/gv4lvsWBVoH4v729+z7igRIC2IKY/+++ToAGQmuBwz9G/k3/gUJygwwB8j6q/rkBQEPownY/eP5uP3rCeEMKgUG+Y/5pAXXCwwKrAH8+t77pAo+DJoCxfuV/vUFqAvVC4UDUfwXAzwM1AYy/K772f1TA9ALTggM+yD6ggF6Bd8BXwHO/fX+7gefDUwIr//I/DAAYAMzCZUFbP3Z9w//JQWZBSQBpfzb+un9dgHI/iP6HPz3AyQEJ/3L+6r9iAHuBqcFj/yI+OQCaQdzAs37wPuc/HMB8gO5/Dz4Z/2TA/kAvf6D+5z2Gvis/uH7H/dN+mT+J/uz/Xf/tfjY9xcDHAOp/Yv+KwJZ/hIATAaeAZv6dADTBM37ifdh+xb7Jv9aBoMEyPuK+3IA8v6V/7oAiPxj+eQA/ATo/2gAp/6m+MD76gK5/oT7YARYB3oF5gY/BAr7TvnUAjQBgPyoAUAFCwICBpkJ3vza/ZwEwv11/ccGqP6D9DgCFglHACv/7wYX/bL4ZQnnB6v2AfwWBpL4hvhhCdAJYv36A00FNPm1/cYGp/RE7pQB9wQrAm0EWgEE+PEAQAw7A5T8wf9i/Qj3SQHyBLr8Uv0oBScBmQTECP/98/vjAvADQASoBf3/8/w9CusNc/+Y+t4JIQln/5QGrQIx9Af/OxIqBBf3kvwU/xH6ugT8Cxj/hvxIBgYFk/xF/zX91PZh+87+P/nR/FQEZQBi/jsBQPrQ+D8Cov5d86f6LwON+gf0OgIbAT71/P7+CCn9b/ZyA64Fgv7jAjwI1f4C+hH/zgCD+F75BvpP+ZL5F/3f/XcBugIA+br6ygN7+Lz0sgBYA6f4wgKqBUv5ffWyA+cC+/KE/EkM/wMs+U0BqQAO9lj5CwYwA3X6OgH4Bmn/QvkJ/d4Dzv1c/AIKxQtCANH+CgjSCW8DOgNAB9oAX/uhCGsTJAbt/GEKfAm7/gwE7wN1+Jv5rgeXB03+MP+h/+z7Bv/tBg/5yvl/BRUCzP1pAm36y/EDCT0O2/et+AMJkPob8UwE9gNw833/UA4U/g/55wZvA8j59wMBBuz78vwMApj/x/5C/of+zv67/ZH4uPw4CDv+rP1YDH8FK/brAfMLTvvI/MgKVgU5+rYIshBoAEf6kv0Y/UL7LwXhAw78/v4pBVIGbwezAQX6NQBwCAMCdgAdBvT84PxHCR0JuABl/F0EPwaPAd0FfQwKAWv2fPzcAUMAnf4iB7AFyvqS/CEMWQnW+6v7kP+cABkAXwgsBxH2PvMp/i4FiP9U/SIC3/x69Kz4tgBX/Hr2nf5aAt76PPqNA4IBivQ093QC8P9m+pb97v+0+LrzAf5OAmn3YPMc/vf8vfMK/hAJlP9U88b92AXh+rX3awEZA+71gvwVDK4AY/JG/DMGxf+x/M4HbwgA9/b33ArpBc3x5vpTCQb8GvNCBuILAvRW800MLgeN7Q78mxPD+g3pOAlSEb7ux+4XFdUOjO88/XYYAwBX6R8RhBrE6ublVxO3Dfbqu/sDGQX8aOSGCXoaNPlY9GoV+g3S7/L8fBa5CH3vif6+Dlz6rvDrEdoXw/ZC/BgRswMh9owKURVG/e7tp/xSBZP9PAQTFtUKUu8t+6oT8goU/j4KXg0++ZL6XBKnDGD3w/2aBRL4VvFJAxkMKP4U9GAAHgaX/mwF9xRDC0T6hwUQEz8Fn/5iC88HOvmn/jcNRwow/twDKwrx/YH4kQI4BML55vrUBsEDsfKu9TsEMfuO8Vj/y/8h8CDxMwCp/lPyD/hw/Z7zf+vh/M8Jc/0e9Dj7tfTu6076MQny/l/uuvMp/Hj8LQD2Br/7Gemb7WP7m/4e/bH8lPlQ8ZfzKv0mBl4Bg/s3+yv6GP4NA90JdQVm/Lv4EP+xARECgweDB5QBi/3dA2cKYwYCAJwFoAQl/mAHtQ9OBoT7eADXBAL/7P5wBfn9EfLx+gUGMANk/w//zv6G/IYAjgqpCub/LfoXALH/kPzkBDIL0AGY97b8RAPs/98CKwr7/87ygPssA7X/VAEuBLkCjftc+YIC5QXP/5sG2gh8/m35Hv6CAj/8Hv0eBHcC5fm4Af8MEgVrAkMIBgSj/dwBiwuACF4AUQLPBxMBQAFxCvgJJfs4/K0DvgBdAW4LNQyZAf/9wQNSCOkAiwZ0CwgCyP17BF8H0gFYBiIHcf7++tv+FAEt/+oBG//7+SD6c/xC/Vz9EgFj/qYA7QNuAyMGtwMDAtYDLwFb/5//j/sv+c77z/3T/AP6+viY+oH1QPV2ANj/zfygAT0B3vmZ+YT97f0Q+8D9MgSyAaP3tfg4/un4GvhDAW4BMfgU9oj9lgJr+jL+9wpQBAv4WwBWBub5nvbVAfUBrvdG+ZIFUQPK9537RgR9+630oP0ZA8H2DfQf/m0Crfoi/QoHtgO3+KP9tgktA5n6lwJIB4P6oPgkB+0JLf6q/RALOgd9/c4F2hNHB9L8fgbvB635SPenB/QHf/YP+9oKOAJo9MEAjAvz/T356QhbCTr2o/nuEX8LEfoEAUoKy/5Z9sYJDw7T+kL2hwffBxn2vv5AClT7Ve2fAkMP/Ps/+EcKPQmB+a4E2xM/BBL3FwLmBygAlQKtCz4JGPxI+l4IIwtN/poAcQg4/8j0vAPDEFkBivhrAzUB1PP//u0OZwU5/ZYFrQiB+w37AwLtABb+wwFjBnUGDP85/c8BmQR/AQYEqAmSBVn9LAAFBpn96/clAmQD2PkJ/S0GKwDV+GP82QAS/UD4h/wC/hH3pvX0/UYCMfh689r8H/9L+Wf81wXl+37ufvllAZ34lfYG/2H6kO+g9NX/kv3H9v35cP3w+ab11f6RAQz5ovRt/Q/+lPYe+aP/Uvs28yD5ov6t9pL0FP0WAB36/fswA/kAH/sJ/ewFmgTf/2EGsQtC/z368wS8Bbn9rwFHCTcDdvvIAGgJPQL+/qkHxwR3+xT+gAQDAcv7gAG0A98Awf2YA00Cpfzv/fwECgbKAsgG8wZBALH/CgJNBMABwQSFBzEGdABsA6cHqgGyBIwGRwUPBHIHtwmABaAChgS+BVUCggP2BZP/U/m9/DIB+AFEBSAFf/529736RgB6/5D/CAV3AKL7DwLJBhgCwQMmBogBFwDzAhIEWgBI/s8BvwN3ANT+xQEZ/6X8RwBXBtgAYgDkA4IDUwAG/3EF4gID/hwAFgJQAMn64/8aADH9nvwVAzcFf/8x/d4BEv+b+Gv9egKt+YjzzvrrAA34ovcv/OD3IfSe+ysGbwEi+nD8Ev/f9nr4uAHi/kb2xfwzAzb/bf07/yX9hPpk+hj//QC6+lz7HQHm/jv9Ef8b/077Lfnl+wcBRgCI/+ABFQJS/sQEZwfNBJQBqP8nAdsBmQOMAZUFL/9X+ZACBAX/AIQD1AdXACMBfQXpAiUDMQKABJAEMATVADMArf+e+xL8WwJfAzoBbQHO/+z8gvwxAuIFWQE1/4AC9/07/OEBjAODASgBoQG9AX0BFwBiAKcBHP25/fD/XvyF+k/+df/T/rr92P2hAccAXf2JAOgAG/vDAAkC2QBBAuYE1gTuAdb+dfxEAcX+Av84BTsDr/vz/AsCd/8CA/cHQgLL/Br9mf+FAAYB9QKMAs0B7QG6Ar4ARf5u/k7/XP01//n9qv3yAmoCqgQ8Bk8AFvvV+lQAZwDDBWQGQABm/E374/8iAXsEwwR2A27+2v8QAzwBxQRbBT8D5QIDA1D9Df51BeAGQwfyCvAAa/ru/Vr+sf5a/9j/nv6h/Kn9qACTAVf/HQGT/if6a/nE/Ez/if/G/eL+Cvy09/n8JQCt/+z93f0rATb+jP9GA64B//2g+8b9Avip9hH7Lf/yAO/7Uf1N/dn31/rPAP4Dk//r/6MDg/2W+dr9swIUAY7+UgFT/kb2Zfi7AhgGSwESA3YGyQGoAJMHJgwLBfsFWAixAwEAAf8IDegL9wTVBBAGyP+h/NMItArGBLYDlAZmBWb7Ev2SBLz/BvysAGABB/kU+fcBjATPAAT93ALpAGr7NAPJCqoEYvzYBG0KbwD5/nQE3gGG9JT3kgEb/Ev2uv38/oX2b/c2/7kCBvtc/GQD/wLC+Oz5UgSv/Bn44gOJBon6Zfnr/8H6svTz9ZH7Ofof9g/9KADL+4L4iP3eAS37+fuGA68BRv8aA+AI5wPA/6oFbgbfAy0DKwb/BL79Lv6kAAQAZ/26//EBcf4e/yoCxgHw/GH7CP5R/pEALgP1BXUCUP6U/9kAoAFpAIgBuP8d/F/+pQC7APj8Mv1J/QX7Y/y5/rX/8/yk/Ob9qf2o/iH+lgBx/zr/+AG9ALD/P/0a/dv9cPxZ/cX8JfvO+Rf6iPwn/Fb8X/4u/6AADwFfAa/9gfpt/GD/fgEQAjYB7P/Q/mcCowSjA84Bdv+X/1gDoAefCsgIHwVNAjkCpAV1B6wHVgZ0AokAfwFxBW8HmQb/A5P/Tf36/jsDgAf2B/YE7gBq/mv9CwHdCPQL1gmrB3oCEwAKAf8FSgoyCNMEKgI4ACoCzQQ5CDkFxf7t/Oz8Xf5tAakD7wFI/F/7Mf2a/kX/af8R/F/3ufYF+Kj7J/9//9/+S/rn9pv1FvmF/WH91Pym+cn4fv0NA4QJ8geuA7b/Kv03/wkALQPF/Uj2nPTr80D7OQGTA5f+6Php9tX4kwKpBLMCpf44+xL6I/xQBU4DBv9O+Jr25fl1/LoKIQysAT7+dvmK9TT+GgsMBUEBPwWd9MTyeAbTAR/3WAUwBOrv2QDHE5z9zfXpDIwAnueoBlcQve4s+0YOkfgK8aQHeA7RANnyQAhlFT7fvPFoNtbo9s68QoL9pK8sQhwiOKFPLB8x5pyoGSNHsa1qCCpEd7lB+0FHSsvb9LFC8cvd4g1HFds+3ndOqOoSyZM6EQWLzIUt4BtQ0CEKoR5969kIzxx9+O//gwr9/YkPPg5L+hYEuwxH/DX8ohiaEQLud/8mI3P/fO4pG1QTI+Nn+9skFQN94L4FHCLX51/tyEBOClDF7yDMKMLG7gAnQjDdV9N+ObUGZ8oqG8Yp4s8O4WMw3wD906kfiRyHzd34ySs06QLlqyHn9ebQXAqICyrgvv2EDpXoqe7CD77+z/DOBmMDX+xl/IoNDO018ZANJfDi7woWxv/O5/QIAwSY4Nf7YA707WLsUQn0/2/oHALoFtrpeeJZExv9Q90DFMQWwdyd/QkfjOzr8wsb0/dm6e0CEgNvA5YApPsyCi3vhuutGN4E/OwhCccD/evR/4EW3gw5/PT4sw0TB4HtjxqSHmTWa/3UKKTfOvUsNcjtPd55HWECT+lTDcYXWvXf6LsOEA/V6NAFwB8t6A30PB8q8rPvIAsc/V/9ufrICTgew+a08yYzSN0N4BpHOeZlzpJJUvRIva464AqUw/ohviJE33jxMhQ0CKbk4ACpOJztRtviNF/0Gcw/LTwW79RVCwEeWO7r+KAd3REB66r6dSIk9bLm5B+jALfboBTmEt7vXwFWCwj/wu0QETYar+IIDb0VS9OVGeEs5c3aE2wlBcIkCHw869x36Xs+QPhcxmo23SIsuU0R4DY1wxDvhj9F8zPTbBkRGBziBfoBK7D8L9S2DpsV+9qs+2wg3PFk4P0NsRHU3rf89SNe6rnwyCTN/OjsUwRN+Qj55+8XBjQY1+hA93YWdt9L7WQaQvJr7TMQ9/nT8hsFTwfcAGf71QHcA9T0APriCm/0vvJkBiH0qfLEAhL7bfks/+n/Ogu6+a/5rgme7OfzZhOy+m/32woz/hP8DQa5Ef8JKfLCCfUHIuhzB6sOr+e3+t0PlPfj94ALyQHz9IwHERJH/Qz+7QKe+V74Hv0GDlgAlvLLDksBOfNiE3gBPPKOCGT4BAEMDCP3KggIA0jxPwjbBG0CJA5OAUILuQEu8gsR8fjb65oQqvnF7XwLkAOC+QD/RwenBGrw3gREEO7pGAXHG8349QBREmADnO5D/gUXtOmz9o8ow+ra8bwmrumI6WkeTPdY6XwQAQhi7Mv2VR6o8oDrYyovAFHYFhgBEQrXmxeTKWXdOv4PGo7iYv3bFH74WxpH+AzyGzOkzWT3jEN7xecDMj8B0Xf4+Cbr6dL0kBD6F0noJO0vMHLcz+J/SYTiBtX+QgzecMusQ8TuttNERSP9tsfLMKgEE8ItKJ4bGMXxE1Ehs8wT/8on5+Pa8WEni/lc3IMczwmd1vkUYw+W2LEG/QUd7ekG2wHCBIwDb/KAB6kASOuYDrkHou0dCj4Bs+9P/xsG4/6j/BgMHgIF9bP93wK8/6n52gYi/mXvfQO5BDD6NgCbCBf7TPmDB1X/6vtVAYQHWP+h/TUNB/Un85EJb/St+XUI1Pvr+9oB3vmE9Dn92fyP+/QAJAKG/MD9HwQ4+OEDZwjv9xoJmAIG+bsFuPe+9ggDqvrn/0EAb/od/Wn52P4J/e/61Qnu+Gr3vgwe847+2BLF8if/qgvd9CAA+wS++YH9ovoZANkDpvp8DlEBhe4yDRr/EfUzC+kBUP4OCaIKWgWs/ScCbgLS7doF7hFx8e0JlgyP4RgFlA3g7qQTHhEk+dkF+QTz/aEBxwjU/Zr6kgQ8BDMAOwI4C7H3T/hjELbxl/yHHLLv8QoTHJjlbgwhCvnjGhMgAkLraAxg9Uj2Lg/K7FIMBwsr5xEXF/vP43UfA/R35yEhs/b46/QiCf9r7DQf2ARs69sSxQnV5swEMgl646X1wRPl5UkEFBsR6kH9PRHz6J8B5RYm8kEENgtP7wIFNwii+A8SlgCF/+YOavdaDTcFgfdTEFjjcwYqGjDQdCBpIoLElTKuHmG0djnUC0i070Wf/c7JJzo97lzPfB0v9Xblvw2bAsXxRfgnB472rgFRF1H7uAJYEeHkaQYoENvkWw0XHcrUUQ3QHFPLjw9SK7rAVQ5eJ628bA1RL32/FA3CKkvHWxLiD8Dhef4U8YQDgfBd6lo4CN2F69A7SsZe5hU7Hcns7YA9J7/B//Uqx9jNBRYgpeDb/P0VVemJAxgPU+5UBLgECPiIDdsBMwRlAwH48wih+uv/bx0Z6pILvxON3BkUTxE/4dUY6w2Y5b0VwAUM9EQEzv+VB5/wVRBiFZLbux1pAU/gSiJm+hTraiHy700KqRlt6mscBfxR5/gnffLG95MoYeOfAwIb5t5sGJ4NttySJPX0reNtNCDl2/1aLIXWcgqRCh3h7xwo9uL9CRtY2zgMmg/12pgk+gnl5LofDPMo6xodoPBkBSwTteVFD8oFTejcIdb5zfEOEmrzwPQHBpj8ePMbAkkCAPc9CdQBEvURAQP9EPddBgAB9fiC/8f2E/QEBeH7UQJuB0X0cAU69hH70BJZ7KsK8wZD4rQKJP877qgY5gba+G0SM+85+XoM4vJPHjgHCemTE8/gqfBwGmnvdAtXAU7nUwE3+gsCoRXU9sz9wv5d5+cOSQC9+FYHb+Rt/H4GH/zCE0YBputp9or/GvwnFu8Kd/SG8WXx2fxnAeAW8ARi9fD8BPcy/xkQFQBrB0nxAu9TEnrv3guhEnTbgfvcAGPnAxNmEvz1Egxr+C71eBKc96MLfwQu4nn53fa99BEWzAjfAckFieo8/B8LKPm5EcIO39EH+kMCfdurL3YZaOS6GKjlmudNIkv/hB1FBrjg+QhL53T5XC8I5jX4lw8s0AoRYx7k8W8ZLfrz8AQXJPvhFwkIBeHvEir4zfmjKlT0TO8PBafvwgXxF+EAAgGJ9Qv4PgYQBugFIAQP7lb98AkVA5sNFwKn8v75AQAUCS4JcgCd+xn6tv2tBjMNj/t5/rIFuvVpGoUJU/z3CN/mCgJIDcb8mRGc+gHqmwjBBEcGoBqd9eL5VPk7/SAT4AgXB934V+8V/Z8NeAviC47+i/Cz8xUJ7Q0qCiYQN/H89mT/yQUJCyQFhgI+8Jz7GQn9A3EL/ACL+Gf9gPvHC14NSvGTAGv0+eaCGOkDovolEujjtgJnAlH0mx5A7C7yFhMa3gYJwBVB5CgJowCC6eAQjPlA9egFCepGBEoM3/PnDr/1zubVEGH11fcjE0LgePUDCOfuig+pCQXwIgNs9y7+wQ8p+YkDKfzJ6GoMo/4a9zwP7+9N/XEM3v74CwH4EP1v+Bn4uA3VBGP9mP6F+3z5dAAlDk//7O+MEWz2oviTJRz3RghkC0Ty4AnQ+04F+Q0K7GQK/AaX5lghYgLL53kmAO2J9tgh3evTBX0MOetICjIKRP9ADzQF+f3FAmz/dP25AJL+ZwH5BQP+AQZMAK75mAkbBpwA2Q/GAqfy/w87BMv0YBvD/MTrERfm+Mzu9Rqa9h/vig+B/IH6rwtbDC78LPy0EnH26fd6GTL2TvNvGIr0XvGrFn/09O33DSr2pfW5EtL+CvsfBDL31fd9A0IBWwcPA4vz6wF88i0Bdxha9CcGnAMw4UgFSf7U8bwIbPmW9eoAi/cd+wf+FPF4Ckv/TwHaF/jyQP7yBuztEghICn73Rv317Ar0ivSZ/nkIUPOl9dr/WPPvAPYOJPAU+/UGS/IGDEQDa/cG+CL06gbE/70IaQoP7e3+mQRv8fMFAARl6OIB+ARL7xwIcgFk6wMENwOd/BgM2AbZAxwBGAPcCgT3DQKODt3vugVkDWPvigV4AjrxJv3O/GEA5f+k/kwL8frV9SAT2O7K9rUbAPIIB/EcuOyl/U4B9evxAGQHowArA4H9L/3p/Cv4QggP/Zz96w10+g4CGRP39rkErxAh8eEGQA7O77QCbQUX8oAEEw3HABAEmwgIATr/jQqU//b3tQLk+zsBCwn1Asf/TQRWARH/iwfQ/Zj+CwPh/kAKagR6/tcFlfPuA/cCTfpYCsr5HPzNCYH70AdWC7v1OAlX/+D73BI997AHJA3v7UwSbwDq6mEZ9vpQ96IhmPb0/L0ZAO5nBbMOsPPJCRMGq/LJBK/1XPWnEBn1XAiED8rp2Q7OCLXyUxGlAB70GxF6/Ir7RgWA8AUBev8G/JoEJvUG+QsByPkOBXUHGvyr/6f7BPxy8/UG/vg68LsXpOw4/G8YXN9vBSkOtuCKE9gBs+gNFP7qvgP/BTHttRCK5z31gQ7d48YEHQo14i4PYwRh75YSVfR3/kYH/ANDDZv2BgNG9GPs5w5+9QH28wzh8Kn7MA5S9Z77OQT59FwG0AGT+3oB1+8wAwEFkfiVCTr+GPM4AhYDB+/WB4sCiO+nDxn28PMSEa73DwkzEKDvwwr4/bjzbQ6J9nr6dwdj87wEqAQV+9IBtv+Y9E8CEQC197sJHv8u+MYIT/xM9CwMYPZr+NEUxO/xCO8LiOnlFtP+g/LGGzrlxPpiEMbhdRNKB9TrkBsj9OX5VxU+8TAJpxJD9C8RPQkh+R8T4gA3AogOyvvUBAEAl/2iAwoELwnXBTAGQAqr/dUGegu+92QQfQlc+kob//6N+8YUN/YFAUEQOvBzBV8NnvIBDygDQ+7oEYv2Svw2E/HwWQvuDkTv7BTB/hLzTh1M+/EAbxU38KUCZAlM9NkIiQCC9+UM3f22/twGNPOo+G0Fa/na//0CpPn1/z4If/82/CAC7/RxAYgH3fvRCen1NPjICmPyhwEKCGrlSgpNCJ7vJRJs8Q7qYAr48Sj7OQxS8AkCwAhA9jQKZv0S9V0C6/IE9Zv7MfIn/dr/xvYa/tj4xva+/YX7R/u9/Nr+2Pyf/qQA7fqQ/2ADgvrg/9H7hfaYAK/4Xf7I/+j1Sf8/9fLxAPxD8Cr6sQGn9bYEEPy8+WYIOPlxBCQI3frMCcT+SvnpCPH73wYfCoj4XQQp+nf1bAcZ+AL/ewnY8roDbwL88ysIqvoa/fUIJPzlA8QGSv4xDPUFngB0CRX/GQWPB6D///0O/2j7lABYBfcEfwKhArb9F/6XA+ABDQj2Bun/iwjT+2j45ApF+rkJUxoi9eQM0gEn7VcYUQC4ABsZEO7lBbkImvARGrD9MffzFpXsNQM9DbvvJQjcBSjyFwnO+mwApQiz/G8K8vwo/h0FOvpMA/cEewGuBeT84wEZ/iH/ug1t/a8G0Qae+MgE9AOr+qgFRPqN+s8D9Pj5BiEFevzwBY7+Xf2sBH38qAHZ/T8AsweR9Y0G5PrS80wKdv5k/IkHXPcK/IAEHfXCCMr9b/n6Cgv0IPs2CdXwXwVKDezv/ghc/Ffsmww0+lj5bxEW6/D9pwME6KcTygFC+JEVDe3m+3YLT++TEJoJ2fGhEkv20/b3Dwn1JwPJB5HxBATwA3b5tgma+wb1Cf5s9g4DhAKKACwDLPUb++378vo6CJUB0gIaAO733gF4/jEAOQqH+if84QA/9aICyAeI+4oCi/s09NQGXgIjAh4Nt/Qz+hUCBvR0CQgKbfpzChD6HfRyCyz2Sv6UC4vwzQksCGL6MxnuBAsBLBRz8kEBtQfS9aIPrQe//dkKafj0924Bg/jWBrAGz/y7BAn7evvSBsn+yQBX/1r4fviQ/Ub9s/4FBNj6ggmhCJIDNgy3/8D1EQXA/un+BRGa+sT7rwJY8iYJcwwd+tYIG/X97z4QLfqwBbYQcuzF/G4AKfRzELgLnvrTCIfxV/hQEBoCWA3PBjvuMvgA+yMCRBa/Bdb3DvXg69D5mA7MBTkAPP8U6nH8jA5nA+sPHP7r5cv7WPVzA54U5+9s+N7z0+y3F34MOgJWCtXjJ/OhCWL3zxHAArDsQwWU+sgFKxYj9oz8B/gL7RUKKQNn+2sB1e8+90ADQv+oCFb+9/ju+rD3ewMiAxv9oQKp+t74QQUR+oz+AAHE9CYGUQHf964GHPko9/MM2v83BKYHXvR4ATgF/v2QChP6zvDT/VD8rQX7Dgn+tvrH+VD4GgrqDZYGLwH5+KfzbAugD9MMvgwN8GDt8/zTATYT5xbt9ir3cfVG9CsWWhAZ/Ef9pOqx9e4T2AxJDz0BBOmZ/SICQgZrFUP/h/X2/9j8iAwhDeH/Xf1R9ysBQQ0ZCGUIOfsN9Zb8KP2WCTcCzvmIAbX31QazDtf9jAZo/Tj2Qg84AUD+kQi87dn+cAx6/NoOJP9K8akG6vuuAbEPSfIy/RYEHPl+Dr0IBvv3BOb68gHBCrr6Gfpv95DxeQbqB+cFigIB9fv6/P1Y/7ALRPmB9VsAV/e9CEcO1/0sA8IAkv0EDdAIev9hBRv8Qf3/Aw0B2/dX/uj27/cVA6X3O/8OAe72pQaX/HH3QQbj9wz/UhB18iYCLQJK7C4TEQQD+LIPYeld91wPjPS4Cp4FM+Z2B/X5jva9Dc3r4+oZBibtmQXOCrjlhfkZAeDz+g6J//jnsvpm+37+9BUj/CfoKfZT9GT/KxrF+4PqR/io8p0G2hzW+5z1KfXU814N7QvH+Lbt6eTB+/gLMw5ABeLqrOXC+foByBHWDXT1C/i1/bQAzRNsBa74sv4sAMYFHA8B/Pb0V/ja9uIIEA31/MAC1wG2AJ8SzghWAO4BY/jHAJUHTf6F++MAsPrXCSoL6gHRAqr/xQGIDNQIJQYd/9r8WAEuBRQHlwPy/zMDuAcvDCYHKv+T9Vf9RQLlCisJiP7b/aAAiAgGCV4CnP1A+BcBswlqBqUBMf2c+KAHNAgnAnoBlvd2/LsS7Q9jBjgCefQf/NkO1AlGBTH+9PV4AOMJywQPARf31vrKAlEJmQhE/XL3jv+ABk8GCwhf/IH6xQKyBz4KrwIW/Nf43fi8A30EZwKQAwL68v01BTkBrwHIAub3MAI3B10AQwQG/071sgC6ARsAXwTH/yD/CQX7AJsC9P5L+mX/FP/V/5sCCfs2/sgB3gB+BEcFqP7H/C0AVP43/cz8hvlk+Qn+Gv5//On7Mf24ACACTgIX/Pf/kQAyAucFXv89+zf8eQBbAQgB9/1Q+ST7+v9LAQsAmv0Y/H/94f6B+0P95fvG/eIE5QBM/LsBCvxq/gIAefmC+5/+VgCOBZQBu/hN/Bz+sP3vA5r8Avrb/Fj/VwNl/k36IvxMAuAGSQUm+wT0dPam+80FogI7/Qj51ffj/Ef+l/1R/Qv8ugIEB1QD2f6+ATX/7Qh/CJv/gvkG90f76QkRB8r+B/5y/TkFGwnKAOv60vRI/hcF4ARc+6P3BfkaA04KaweA/Yj9gwN7Dl4PSQO79D7u8/kOBrgHMwcPA3YCCwWYBg/9u/3FAZAMWRNCDQn/nPwf+p8ABQrTBh8GJQDw/lkAw/zPAq4EhAThAj8BH/0cAWgHBwveDR0JpQCs/yX+TQOrCjoI6QIDArn62Pqp/TT9Jf4bA9wFdATSACb6+PnI+0cAjgTMAQT/aP5Q/KX9+vy5+yX/dwAd/88BLP5s/j4BW/9e/h78G/mL/gL+LgBX/xz5PPsd/g7+xgLL/xj/FP/A/fr9cv0q+7P5hvvS+rkC6P4u/wb/ovTI/hQCqf/gBVMCcPxaAIMC1fwVAPr9Vf0W/5oCGAF9/ar7K/35/acFLAQL/mf89fhs/K0AMADW+ob5nPcH+OD7QP2N/dP/Yf6X/0b/0/sY/KL/tP45AJEAT/nl9+P8V/+LAYT/dfvy9WD23PwA/ysAj/6l+233yPti/noA8QCJ/Zv8Qv43/jX+hP1l/RsAywNdAsX/cPsO+2cAAQa+BYYCBfqk+9r95QGqBSADmv5h/9P9egCTAbAAPgFUAmABlwVsBF0BiQIgANEAsQadBE4GEgI7AoYBZgBRA7YDygBAA6AEgQIpCHIF//8xAp7+iQFYBTkDIAWgBW4EsAYwAkwCiwNqAZgG2goABG8GiATv/HQE6QJ3AZsFjv5CAQYHXQXBB38GNwHuAWb+IgIYBqcB9QbiBUP9sAC6AI/+swfDBP8A/gMo/HT+sQI6/mAFAQNJ/6YDEgPAAy4FIAQ8Am8CCAHr/w3+XPoG/Gr8X/zT/N77ovyF+zH7Sf4y/L/7kfyp/oADzgMABgwCgv4i/939N/+G/wgA1ABQAygBawGD/3D4TvnF9rL3kf31+tD5kPqZ+FT8lv7S/3b++/xg+/f77PyR/vP9rPm6+VL5cPxgAXUBPgNlAKD+NP/g/Aj/xAHcAM0BPgOzAOf9OAF9+un7OPuO+6P8KP/e/wr/fQGDANMBbQAM/3/9Uv8M/fkACwL/AC4FlgHyAqUCr/3K/xsCSQGxBioLewLVBM4DpgEFBpACdADN/l38c/xWAYsClQGe/lT8S/u2/s4DzQa8AtX+K/7a+MP+NwjxBZAKigOu9MT2N/t5AggQjw4OBt/9w/YZ/CMG0AtJDrMBjfnE+HP++QrDFJkKYQOM+e33AgLYDN0N6wiP+i741/bE/sMIUAju/2v4EPTD97kAMAf7A7L/vfSs8Rf54wKxCYsJvAAj9CnvHPdLAcoJywZT/7X3Z/ay/V4G6QYw/673fvWe+HEBbQgcAwX+nPhf9d352f4hBHwFdAQdALX98fo3/J4BAANZBTsCNAHF/xUEkgl1CNAEo/t3+TH7tQRsCOgFqf669yL2dP6qBOAFxAJt+ef3HP1YBKYGqwNh/JL2P/u1Aj0G4wPT/Qn39vbo/i8G7Ab6/93z5/Lx+CwK5xE1C8//p/IK8woArQvSDn0EJ/dc8v/3/QRdD2wH9vro84Dz7v6eCygMwwVa/ND2uvu3AgAHdwd1/nn7BPdk+jP+9gA0BMUEXQG5+2b6C/ql/lMKXgseBpj9j/UT99UCWA7YDrYEBvoF8g77/AkhDAsK0ftc8bDzUwDBCNYKYwKs9k/1O/1MDdcRUA5v/yn3hPc8/osJLAj1/3P3ofnG/ycITA2NBVX9KPna/OoBhQhQB8n7r/rZ+gb+VAZ0BML9Uvrr/B8B3wQ9BJP9D/oZ/H7/8QJY/2z6gfaZ+gIDowhsB1P+MvZh918AcQilCEUCyvfG9pX9/wGIAhQAifn4+Br/iATFBnkCJ/mZ9x/7mgTqCMkIqv4g95v4FPuOAVIDq/96/Jv7Kvul/mwEZgLf+zX6JvwoAL4GpwcgBAn9HvzV+bD9ZgCdAeQAivxZ+l3+AAP1BiEET/wB+R/3XPs2/z8AaQAZARcAz/43AuP/JQWQBTEFtwKa/yYB5wJkB1QJ6gU7AU39of3GApkI/wiXApn78fX++W8AYgUvBgT/OPvW+k4AtgccBc8Dm/ql96P6YQI/Cu8HxQQ2/un+ZQN5Cn4KEgE7/Mj4Kf2mAlQFhwKB/T/5KPxHAj8EoQbmAVX6hfnS/d4B4QOKAhEAsvpd/Pr/dwCEAYMAdPyX+Bz7L/0uAR0EJAHS/Tb59/ui/vIEnge/A0QAZQEv+xn6dvpL+ef4f/zj/an+cgRxBhgF5gGK/nH/ngNECKIH1gJt/8b+1f/VAaQC2wK2A2IBcgKzAhv+vvxn/Sb73PwOAOf/Mvuh/Wb7bv0mAVYBcgQIBccGRASSAbv/3/0xAqICvf+o/jP+ZP4oAPj+If4D+5j+KgBOAO/9e/t9+fj6Xf7A/NT7gfnq+Wf6pPh1+C/3IPlr+6L6Z/sF+0n9tQLdBIsAxwLWBREE5AR1Acr9FwGuBuQGgQVI/Xv7n/6oAaMFMgFB/c768fsb/5n/Sv86+w39w/ng/YUAuAB8AxwEnwPBAEIBdAMhBCsGTAXfASwAMQTRA/oBHP5q/nX/0gR5BosDcAHl/wIEVwk1C9AJJghxBGADXQFD/d78pfmj/Ez96QCu/+D+MQFnBHsFswSXBQr/PgC/Adb+wQBrAfQBnAK0A+H/IPxC/tD9bv/YA7UDPQOKAckB+/8KAFIArv0mAJYAyQP3A+4BJP97+hz/1ACoAVUDWf/n/m//YgMyArUDBAFZ/YX8ngAnApQEmQQwAEn7rvu7/sYAHgFfALn7uv3JAO8BQwJvAE3+L/vw/7//awLjAIb9Sv69/8f/OAEFBGcAiQAFA1b+av3p/Cr66PuU/Pr9GQAnAD8BpP+r/jgA9P8HAPf/Vv5x+6H+ev90AgYFqwAp/kP7G/un/lACrwIJAicAJP5c/TL8XPzf/Nv+lwIABJoEcALoAdEALQLFA74Avv8+/W76S/3U/D79JvxD/P77Wv3NAXT+3gI4ArsCkgVTAzYDKwGm/vP9nP81//v/xAFi/+X/ev/6AI0CGgJ1AYYCKQGnAgcCSgC3/KD6GftX/jgB9QP7BEgAdgGQ/vf8dvxi/Wv9kv4SA3wAWgAy/8v8Sfwe/eH+iwAQA+UDwwB6/pb+KvuU/y8DIwDn/R/9ofyf/6gDQwBv/Az95/2cA5IGfwQWAJP/QgBGBAMFHwMt/fz7iv3D/HX/Lv5q/W388/1S/NL4Bfw3/bMAewNNA+gAHv/v/sj+rgKvAqYAZv8t/ET76v1r/aD9SP01/NL70/tG/v3/ZQEjA/kC3wFYACIAd//4/lr+GvyR/IT9Gf82/v78Sf5z/oAFNQeBBEMCr/z8+1n9Nv6Z+zD+Lv3tAC8HtwNXAzj9GPzS/roB6v4Y+vv3GPlF/G8AYf+2+k36Dvyu/2UAGAC6+5H8vv+ZA0MGPgSD/v39QQP/BUUHGwaZ/9MA0gCwAyMC/QD0/6AA6gRCBd4FQQKwBMoCawZXCNMCdwWuA6cEjweXBt4C2P8XAK0APQPABOEBPwIAAkcBNgUVBFgCnv+yA8kCGwO9Aq3+4Ps5/gUACADL/kwAlALlBPAIcQaR/+H+CgFCBFUHiASx/W/6Kfsl+mX99f6L/sn/0gDhAioBBQT8A9kCowUCBCwEdgHZ//38Qf2qAL//D/+S+TL53fi9+tT9Yv7U/Vv9a/7k/hP91f5c/Rn+WQEBA3YFXgXlBMsFegVwBPz+QPuT+cb6A/1H/3b8a/aP+Ev7R/8JAjoBSQCh/tL+QQHPAAMBWgEaAMz/Kf7b/G39CP+b/vL+F/3c+wT9Cv31/PD7dv4EAHsCkwSaAbsCXwGJAD4AXAFUAJ4BFQMU//P/H/9A/pwC7AGr/7j/Pvx1+rP+0f8iAa//Qv+OAGsA3wQtB6IFQgSFAwP/If8OAQoFAwc4BpgDTgCm/GX/9v/R/KcBmAB3A0YEkQPcAIL/iQDe/kYAW/w6+v754fvS/VH/Yf/9//r/xgA6AbD8Yf3r/uX9iP2/+jf3Q/orAAYEeQXoAaz/gQCJ/VH/rAArAIn/CgF1/BL6V/r+/CgBwwUyCNcFYQMqADf/8gI2BuEGZgOO/+z9MP9ZAkMEdgI+AM//1//nAWEExwRgBnUFmwBv/aP8Nv9oAwwFjATCAEv+TABvAvYDLATKANX8Nfpv+w39dwJdBLMCvv6p/ZD9uwC+AXT+eP0T/Pv6BAG1/a76tvky+vf4Evxa+/L6cf3I/Y7/xP+mAFf/4v0u/J345PcJ+378Qv5E//f7l/h/9jj4svo8/Vv+pv5G/bwAeQDR/Un8u/znAEYF8gYaBIkCqAOdBM8FHgPX/yICjATXBbYDCfw3+Rn7a/1IAxkGvwWABGoBC//4//kAjga9CUQBz/zu+Kj7KANWCiMHKQDa+0b8EQCyBVgGuQO1APj9df1A/0wCmwPGA90CrAGbA18H0AkXCRoFNADF/bwA4gRAB+wGLwKnAUsBAgTmB1wFkgIkAEYBbAN/BWADQPzj9g32BPwAAGoGjQblAoX/M///AKEEtAjnBqL8lvVH9Xv7JgXrCOgA/fd59Tr3QgGHCOoDsP9q+Tb4pv0JBQoIoAPX+T/xPfIf+2AHVQktAyr37+7L9sEDaQrrBx/82e3t7cX37wFZCK//JfQh7ZXuBvxoCPAKIQFM9Kvs6/OMAgwLCAgB/crzM/TP/RoJkAnp/8z1x/Gy9koD2ArTCFYAKPoE+t3+cgQpBtcCa/8t/uj/4wBQAxADBQWzA1sBeP2o/YQAjgIcB+oEyP5Y/Ar8GAIhCjkNlQoSBOn7+PtcA5MMlg39B2H9W/nE+3wF6QvPBj37rPEK8079Qgp7EKIKqgG+/Cn+lwUOClwIPv489zz0I/+CCeEOcAjs+bHwW/fmBIYQ5RGbAkXx9On29IoI4hPADCj6t+2A8eUDCRKCE0kF/e4A6zP0SAYFDowJK/bX6aPxGQG9Ef0PCQAC8hbySAGCFJoZdwkU88DqLfR5BI8Ocwhd9iLrUe+x/uMLMgyh/mbwV+5Q9/YHkBJpDKj8l/EE8z/85Qh6DKwFD/v59rr7dQMKCT0HXP6F9Xn0P/3JB/kJ9ALu+qj0bPhgAqUM2go1/Wr0ifZuAMoIFQgw/wv0ivFs9yMFwwpoAbz0MfCB+BIHPQ9TCQT6OfRV+7IJ3A3wBVP2mO4B8l0GDRPwD+gAdPgB+tgDuAtMClcBUvcN9CX8xgTTB5kCfv4l/kUCmQY7BowEGv/5/Dv9Ff8TBgIGcAai/c/7X/cW+ob/GQVNARj7hfYD+boCYA21ClAB8PgY9p785gsyENYKfPzU9Af3RAPbCp4FDPhk7sHxXQCmDF8LH/el71f0DwiQFDURiP5E79HykATSEZ8OU/nl7Q7yYwN/DJwJWfrP7gjxxAHbDHMIS/62+Yb7+wNFCssGD/wG+Zz9uAyLDEoFKPdM9Nv6FwU8Ce4DT/kA9/EA4w+SEOAIb/i99Bf5ggQrCikEm/iU8xr3vP35ARMCTP2h/vkA2QWzBV7/8/ei9+kAEgXABtgF4/6N/mH90AJZAtMBxv1uAbQDPwBFAIn/q/9U/Zn9Kv2a+Tr6gPs8AuUCBQJXAN799vqf9xX/EgXLA6n/Xvpx+e/9lwOBBuQGh/8z/kz/+AAC/qr66/kK/CH+0P31+3j6eP3uBPAHKwgxA+v+yPwjAlYF2AW9BEcCQwAE/hD+lP/u/Ln9sgFoBV8DIwTqAJH+fQDA/yH/lf2b+kb6Vf5bAn4CdgR9AgX9uPqp+/D92gDgAq8Civ7c+7P9UQKIAHL/VALUBpoEo/we9pD29v0wCEcJx/4r9rD4bgbjC5kIwvuG9Bb75QjnEcoJCfvO80395AlUDOUEtPix9r4BNQuVDJcD9/rX+lECawfNAgn70/Nk+A8D4giIB03/Mvye/XMFeAjdAtH8w/i//3gDvQHl+mD34P1NBwENBQiD/qX2DfvVBGYF7P5a90PzWvNC/oQEVv/m+sX8qgANBkUJ3AJj+0X+VgLwBNICov6r/KD/hAVKBLj8Qva89Cr8MgLiAhD7Vvkf+rD9RQEYAKD9yv4eA4UEdAC1/bv7svpC/gQBFAJ+/9f8if2v/iAAwASCAvP9FgBbBbcHogrpCeoCVv5xBPUJ6QlrBecAB/2w/e0EigcpAX39MgBKBjAKqAv6Bwn94vkiAfsGvQKf/PL7ff0+BJkIHQHg9i/5ngOPCIQCtvtf91b7WgSICM8DVPoy+t/+wP+C/Ar6m/eD+34EUAiIBKH8sPuA/vv/YgNtBWQDiP54ABACSwAj/9b/uPx3+iz+KgDNAR8ADQCoAYv/jAHtAvj/0/xuANACJQBXAd3+nv1X/JUB+APzAMf+Pv4E/ij+IAJUAfn8av5NA4sE2gE2AGb9v/1yASQGjQSC//D8bP6h/tz+8gETAhsCZQSKAhH86vzSAQQCgwCx/r395PkA/9v/nf3k/WP+JwAR/9z+i/3z+nX6av4A/7T98v5Y/aj8jPvZ+4385/g1+gD6/fYk+7j8Nvqx+2H95fui/AMB9v1C/l79+PvD+VP9+wDG/lz+p/4w/Ir7BPkB/Ff7G/0+/ycCfP7I+hz6Xvrr/YQFAwZi/cL5wvqM/vICGQR1BD3/MQCIAjQBXQSDBEkDJgAMAKoAHgFpAF0Aiv81/f3/lgB9A0ADnQLFAwkCngNABl4FDwZLBdMEogJEAeACUgSpAoADCf+mAGIBEgUPDOkHkgNDAwgC8wR8CakKXwc6A+kDawbMA7oEiQN7/j0CyAJ8BXcGrARGBRsEAQQ1BgQIdAibCGYF3wFrAPn9wP/8/5D/N/0V/Pj+P//QAZMCGATRANr+FQA0/NP9o/7B/Xr8Tvpo/gn8A/er+yf+2/iA/pX8ePqg/mD/IQJy/fT63/3o+h38bQE0+vn2tvjg+DL/o/55/Lb7k/Tt/sgEs/9tAtr7evvf/tv+UwL8/q38CQEl/qT/ZAC8/S4C5/6xABcBfPzM/WH+tv4EAScALwCwAMj9oAAjAb78XgOOAcT+vgBc+Q/8DP9S/9wImQGm+2QAwfevAIoEfv2QAsr6Jv8RA5L9BAWu/o39CgWn+j4Akv6U+VgFEP3gAHIE/fbeBD/8CvpxAuD0nAOHBFH8SQot98T6zgVr9qIK7gQ7+sULJ/rWBZcPSfv8C7r/I/e7CZ37vP9sBmj0LAOEA1z5+g9RAHYDXBBM9KEH1wWc+PYSNADC+/UG7OlI/icEY/gZFej5WPlUBejt2Ay8BxL6LRD+8oD9yAE37w0IQv1h/rILWvLVAWv4Q/fVEKz9Wgo7AtDtLga5/kgNMRfK+scBAfIU9OYNtwC3DncE9fDY/b3z4f9CCqP97Agn/iz4EgMT+nEAdAc7/hcGu/09+YoDgfrYArUGJPniA3kAwfq0BzP50P0aBIL0AghI/vf3WwkI8S3/vQUD8aAPOPtu9koNLO/XCL4N2/SkE8H1ave8EEHuCwuF/6roHgq27acBtxCZ7lwHePlL9PwTnwFzB44EQPAHAOUBNwUADfgBfPuk/+sEPAU0EecAVPWBAN7vPwmbFOX3PA0H763ruBJQ+Z8W7BG66m8JdPMaAP0e3fbfBE78r+dHEy8FtgUtDgvoXPaE/uz5HBXaA8T2cPct5vb8aw7OCIoP5fIN5hjzy/M9E/MWS/4J+VbkIvAZCoYLUAxW9d/ieu5g+HcM2A+F/Pzzye7A9EMGBgkHBEn+h/f7938CIQNXA58D1vkw/Yf9XPgsAoT8Nf5aBn35dv8e/sT15wfUAHcC6gnr9u0CgARY//4PvPzx+on90/FaCP8Eqv9QBr/yjfyuA6P7kQtv/4H62wMe+4kGXAQS/owDTvfQ+3kBP/zxBIT/9/th+bL4tP0+BjIGcgJ1/tH2h/irA08AqgNeApP5PQNmAdACzApQ/HQE4gRK/kwMSgJ1/poD2vm2AfMAx/pOBGT92v9NAkH7zgHn+oQCMAgyAgQIBvz5+tACiPxqDgkDYv73Br/zpwiwCiH8hxB297/2GwfK8uENGAsT+V0MTvZ4/qoQyvykCmcDjPJfCHf+lAQNEfj6TwP/A+D6Dw1MBL/+QgPa970AXQXY/wIFeP1A/fsBFAFxA6oF7AMAAbP8H/xn+6cCSwlECHkFNPv7+Pb+UAFqB84IgP0G+Vr4d/sYB/oHgAW+Afn2A/0NBg4D0Q3QAy/8SwXL+vcCJAmV+98GUP4B+awFCfyY/z4F3vcQAZIAV/qVCDT82/7iA0P7AgUkA3T/uwPQ/Hz50fz8+bf/HgK9/Hn32/WF+EX+XQeyBCcBJP0E+pL99/tR/Lv+evu3/qz9afly/G/8ggGNBDv+h/1A+Fj4UgXr/pIEMgGm8+kCIPw//6gK/fciApoBHfcwCvb+MvyjCUb5QQX1C4n9CA2zBUoDxQ//ARADDwVL918BKQCS9V8Dpv0bAZwMmgBWAlcASfg4BbAEGwGjBYr9OfrIBLH/mwLmBGH73P8u/3b72gU1/1QBtga2/PAC+f4O+gf+//06/mQDMgE9/3QAF/6J/5n90wJn+fz98Pgs8yr/y/Zh/vYCd/rfA5z+KvfjAS36HP69CxP0qvzm+PfrGwlG/6X6/AV07s72sQNK+OoINAcX/NkC0flN/NMFjwLj//D+wvihACkE7QBqABr8svr/BfMH8QD5Adn99P+UBj8ATv+h/7r8mQAFBLP/WwGpAmj9AACf/+n+qwYzAjj/V/9W/50Eqga+AXgBiQFDA9UFcgHJ+1T9zQFv/30GMf9I+w0Cyfg//Jr9tPaOAJUDNfndAD7+P/6qCW79//ak/vXzCwNNCcH0kAKC+nf95AwiAET/BgXU/q8DywuM+o4B+//59AgEU/rm+YkHLvgTA0MCcPtqCKUCXP6iArH/sAIFDMQEkAERBqX33QUKAAv77QCy+XsAd/+b/3r9LADXApD+DgIWAPsCuwOLBh4AewFyAVgAAwa8/Zr73ATNAPoFbgX/+hsAXgZvAnsGxwGM+DEF4APDBHcKFPtjB8MCagEGCDP5cfmzApT8L/9vBpHz3ABMAvr70RGTBKUAtQyB+LX/WQi39ssCCgDD8JYD/AFC+p4OYftz91EI4/WJBoQNvvmcA24Bl/XGBloEl/vmBz7/AgB3C8P9/AE2ApP2cwCg/wj4wgPDAaL+RgYnAGX0VQQqA4EBfQha/BD2Xf81+V8ARAHP9w/+dAExAZUB0QVe9VMBVQAp88kIv/Y0+N0KrvOFAIQFPfFLA1AE0fLZC3j8E+7dD2bv0/YCFAfn2QmtC1Dl+hhd/sbuKRTS56j1oBHU7pUBBQ8f5LMHpA1V7TEXxfi87fASpPj3CGcRQPkY/5kIgvdUCEwFdvKeA1j4mvo5B0X+uwAU/c3+B/2ZAgACtfnBAx4EWAFHDCUGmvgVB0n/DACkDkf3LPnjBB/1NARvCXfyzQg4/uD5HxLt+jAC1wlt/GcFbQJD9337sf6Y+8kGBv4T9VIBoPwIA3YKy/ow/4YD/v8aC7INK/ipAC/+vPIYC/T7/fYrC473Z/4nEybyOwEnBVvnBA1t/fT0bxIj9bn6oQxy87H7nAVf8+MD5wkT9BYHt//C8NkK+Pmd+TYNTfYDAg0MZ/YlCaYH1fLSBZT7l/kZErAChv55CmrzAAQdDwQARglqBWr39QhjCBv9rgzC+lbzUAnU9YkC7Q2J8VkHqgVX83sUlP7Q/H4U4PAJCRQNZfIIE2D8jPXvDgXyTPxTDirqsAPeBqTntxR0/3z2Px6h83gGRRXx85gJogmN8kIJJQXw89IJhfyV+NIIbvmg+1IA9vMr/koDRvxjBMP/tfgNAkMC7vkcBQr3zvVGBTn51f7CAlLxdPx+AH70xQMm9zfsYQMH9nYCPA2+8fMBSfsy+a0QU/02+zD/fe8Q/l8JsflW/Er5X+rU/4MBKPy7Ar31Wvi0AqEF7wH4/mn5dPrrBzAFnwL9+/TxV/3TBzsGYgpS90Xxnv8A/F0J3grg9Qj9Yvol+bAJXP7J+eL+vvdSBSsKSgDXBUn3+vwuBdr6nAhO+nr7uAcy/6kKfQhz9/4A8QBN+8sRMAMI/lMNsPUWBzwNMvEQBjH/LvNzFAwDufxJEkf2zQE1F630+gcoA2Pniw6M/9D9IRRI7Z/6/wj98lEPFAbT8D0KkfWV+FYTBPUCBDwNUvLeBmIFWvSXDaADhPxCEOj5D/v9CLL6SQ0tCh79pwDR+rj9MAcNBuEABf7099f7CwWvA+0DWf0I9xX36P03BAYCRwOi/ML2pgVV/6cDkApC8ir7mf1U9wMOhgRY/DMD1fS9+NwL+v1YBxsM1PS4BoYA9fi7Dsj9QADSCRj4LgShBmT+2wVz/pf3+v0G+738mgQz+s/70/e18l78rvs9BB4Fr//b/N359fv5/S8I4gKiAGoBjPZKAi4CGvy7/e/0Ru9bAgkDiQJHDhv2X/YUBCH6igsBE9/zaf059zjstg0fBYb+0QVq8n306whbAssH7wwa9G/86P9M/EsQDwrKAH8C3fOE8LwBfPwBAzsM/PM7+5T/cO/jCrgKBfayC7Lx8em1C+Lz4wU7EdHqXv3a+GLx/RMvB5cAEQf158/zIwQ7+gYUtAcP9LkAXvUrAhEWrgbIBp398u8IBR0KqQ8HFzsAJP+k/nz9vg0cBSn/Ofpp8r/+pgVmBgwKbP30+RIDR/6jCSkMP/m8AgD7gPX/CED/6/0dBhz0efssAbT3YwXTBsH8/gDa++/0f/8AAv3/7Qrz/jH+FgeP/UIJzgmI+uX+YPyY9gMMaQkrA0INPPYI/bUIbwEoEW4NR/oM/9/94/3uFFIJav/r/07thv2UC9gELQm2/EHsfv7UAeoLqRRS/Uz4nPWt9L4HqQ5NB/oAO/k19mIARwkCCLL/Y/lK8tj6NgszBZ4JnQJB7dEERv21/jEZ+/gt+BEITecLBQwOhe4sCUj2nut7Elb5m/1CC93k+/x5CDb1thDw/efrDAf59g0AIxSe9hEARP6A8ysH2//e9yQAL/Sn/YYL1P4MBC79B/DyAqH/BwEBCpr3Q/waAwf9WQu1A2/7N/0/+RX+hAHc/cn6r/lT/SMDlgkkCdr+TgJD+uf6NwzX+ikEZAPE8PsMWgvqAaEWBfqd7wcLVPGsAZgUMevkBY8BIu1cE7v9kfehDm3zvAH2FP/7LwhzA8jtGAK1/j76PQuz/M34xwSV++cB2waL+3z76QE4//QEyQzu/hj9vQAH8wYBaAY1+hcKvQFq+jcNJP1r/pwIfvDt/rkDh/d/DVYBFvhPCMT7FwC6EXP9xQQZBzj1lgYdB9/9wArS/tL5/AQX/WIBPQkm/Mz/AwdJ+0kGWQlS9ugGawHS+fcQxv8R/CEMX/IT/wUJhfEHCD4EhfOqCg/7kPUeDSv1/P2HCPDsjQdNAcL3nw85+lL9Mgir8Bz9Zfyq8XYDtP/0+OEB6/XY9xcCpPk5BLwAtvsJBUf+3QYqAfH8HAEV+JgBhwVm/eUFSwGt+sIKOwC0+qEFHPD7+ZoH4/fbBk0EM/PrBD7+E/wQDlX++/5tBIrzPQFj/hn2CwXD9mv2YQFw8yEAlf+e9XMEDf66+zsIOflm/6AI4fc6B2b9+vAIAyL3tfsZCiT4qP0SA6T4/Qa5BCj8wwXdABUBvwu1/aj7Ef1m9QUBCAfe/fMAyf1w9aEDAAW1/88GJ/t/9z0DFABnAiUIu/tG/H8F7ACaBmAHPfdr/WIBnP1sDHwEBP60Aoz/EgIiBrn/Bv6sAZX/wAV/C7gDQgXpB5L/iAqsB37/8wiM/yMBpAy7/40DUwcQ9lAGegNi9+MI1vWY+AcN2PY8BrsIde8pCLICPfbbDUv3jfUcCPzuv/zuAG3vagREA0z41Qgz+zP48Qm7+ZQCvQc794UFmAOu+xgD8/1Q+uQEPv/Y/rsCJ/xsA6sHkwdQCOYD/P99/Br+V/un/LEBhP2tAe4Fdvk4Azz/P/lmDMP/sv55B9HwU/49BOj2Nw5+AWP6oguy8Xv/6AEZ9EIIkPvl+CsHI/fbAHsK8/nqCN0EAfnLBOf2WfurALr9Twju/u38f/1d9zwFnQZ0A8UKRQOE/3MFqfsB/OIDuvzBBLoARPco/Vv25vxbBJsAhgJ4AVf6i/3XAjT+yAnFBzj7PQEt9lP3Zga1/xgFCwg0/E8DNP+K+fcDUwDPAs0GS/5B/6UDXAOJBAQHHv0b/fj76fRE/OL96PwXCFYF5f2qBVP6q/lwBnf3/QIFBhbvIP9B+B30TQ+c/nsDGA7M8j8DVwEQ8/sJuf4Q+z0I1PVW+bcFxvcOBSUFCvYzAr76k/oIBK7+4gR5B38DtQF//179kADzAqcEkv/z/UH/wPc4Ae4BM/2/DGYElf8pBs75r/wpDKkDaQhNCgv4twOMAU75TwspAGcABwt9+dD94wEC9p0IiAWo+/4FMvqR/DMJJAAOBWkGEPpI/1H8WvcMAwIBVgERBVf65vfE/MH7+AHTBowDfwTeBWT/7gFKAWH6ZgTT//b5eQCM8gb22wR/+5EGcQh69AwC4f3j9wgK3/2i+bMD3PQs9ycA6Pf+BVMJwP4MAw35LPXFADP+ogFPBkn9Kv9g/hj4HwBmACn9FP65+PX1kfyf/+L/4QPn/Tb8DgKE/mv/NQRk/bD+2/5m8z77Ufrd+HcDpPlL+iwBq/gEBAsG3/wjB0j/Sf8nCoT/SgUGBF74OwF8+aP4/AI5+nr/YgL09/gDDAW1/VMMrf9f/DwHNPbp/tkDMfdqAsUBcvvaCHMD4f3sBJr4g/uhAg//QwUZBKf9Yv/uAPAAGgidB+MBZgKYAFr9aAMF/wj9YwBj93r6F/4l+bQGuwMs/IsKEfwQ/mYNL/YWBwoJBvVZD8EBovumETv4Hft+ClP0AgWEDB33QAh4Bw36kAlTBPb9eAc+AQD8PQEVBPb97gYqBd75YQUB/O/2WAa//FD8Vwdt9DH+QAlb++MLYgaJ+dUHffx9/JUKmPt8AJ8CxfIs/osAdP2TC64E9f8uBN79Dv9RA07+qv22ABX5C/tX/BX3QgHGBcwCmgWsAZn3GQIcAcn7vQq/+oL46giV+T8Bqg2n9nQCZwvv9bsIfgeh9SAP7gX4/UsTNPjY9xoIC/K+CRoU0v4qDmUCqfJgCVICJAKPDgP3w/Xh/rf5ygsDE2wDSQHw+NT1zAbuCIoG2f2n7n/vRvZWAs0HCwTA+y7zZPNw+mcGPgcCAbH3yO3Y8ij+EQQlBW7+API+8uX6gQDnCJoGcvZe9H7x1fFlBWkCWP2W/bjr4PCV/in+/AYLAxTw8/VK+6L5cQwTAUT0GPus8j/+RA2KBpgG9gP8+CwATQfFA1QGRP9q9pz5E/17BEwIeQa2/6n62wIxBKMI6Qt6Au0ABgRbAeUFdQlC/7r/UQEg/F8Ltg3+A0QHpf3T9+0DbQJn/nYF4Pcy90oEsgN/C5cPHf5N/UMBN/mCB7QJPv8KBX4Ahv89Ck0JHgO2BbX92vxaCbEE7wYKBjP6z/v3/Xj9zAOWAUf58vwr/jX/wAfSBIH9Mf9a+Un7EAhzBPMA6wHt9ov4pf/J/tsBUwAi+Dj2ZfiH+Tr82/wK+fj26fiz+5T+FQGg+4r4MPhG+YMCiAjUA9oA8fvA9k0AsgJaAJYBsPfp9nL/oAJdBb8DTfsp9mP4vPtrAoEGGQEo/6T+WgNxB3II1gUF/+b9swC9BOMJUwhMBbIB6Pow+b/7QP2tAc4CaQBT+LH2UPyzB1wPiQxCAO7zhPJN/sAKqwy+/0L07PGYArsTOhjyBxHwx+nO8R4I2RUOB1TxneLk6J0AfhaPEBb+funH5oH9axLSEbgC7+gM4qT21gw5FogL+PJp7bH5tgzpFvUKPPFv6nfzRwRNFLgOEfoP7jHxtQDMDkUSWgE09Gfu5PU8CA4QGguW/gbw3feOB34U5hZ+BY7wTvJR/AYNkBUBBUvzR/Jr+loR+hohCtr7gfWA//wXRBlKCbLzNuuE+WgRdRTJCNH7OvQoBfAYTReWBanwq+9GAKMU5BTvBDnzx+8C/hMN6Qkr/mXz6+7E/o0OXwk7APD37fbyBD8LdQgV/8Tz2PksBfcM3gmkBI37BP3VBrQM9gtOA9X/fwEfBPoGUAVN/0/7wgHcAgQHDQcL/47/Lf8JAkkILQQzAp8CSf4RAb4Ct/+GAoYBswCk/078cvs8/dj7Kv2++ov2J/g3/Mv9/Pzm+u/4YvqP/gYAwfzM9z32XfkPAFACqQFz/hX5zvxEAlX/VPzm9/71XPnt/U3/bvrY88z1v/yFAEgAzPwD9572vPt5AJP+NPab9jv58/pv/Ab5KPQz8nz43v0r/yH9F/0v/Uz41vct93P1A/c++gP9iv6A/l4CGgJ9/4sA4wAZ/ZT+2/8V/KP+Nf04/24BaQBYAzcBdf4PAv//Kv/x/zX+Vv38/JT9VAItAuYACAJk/rP/MQZkBUkE3f2y/L0AMQH4BXQDr/y9/RoAtwLhBEMAJPyM/R3+RgLVAHb70f1hAPkGZgvFBcgAQgD2AUcJ6QnVBKsDwf+dAQAGigCD/Ev93f1OBHQGkADO+s35SgRQDQ0ORA2xBkkCTwmcDqMLtAgpA+8EgAhnDFcJQwJK/d8CewWoBZICTvwKABcH9QgNBY791ftjAgIJQga+AnD+YgIVCxcQkQdV/pb6kwMQD8oP/gqB/p77LQbMDbwKFP8T9Nz1VQOQCg4FafZ58Sj/8Q3qEK0F0vKl8rAFbBNpDdf5sO058pIC0wooASrxHPFW/tkHkgHd8KDoNvM7BlgLdv+27THsSvinB8MFZPlE7qvuqvxSBKQAFvYZ82r46/8//1f9vPJB9Mr9nwB1AHP8aPeP/Pr9zPzU+Svx4/LL/NACUwQHAEn2Zv1F/QH+5AI7+Pz//P+r/7n45Pcs9aL/kgJ9/rj64fYb/Dj9ywCWAXUBSQAQBJD/N//H/Lv9j/8FAfj/1gCS/hf+YfzSAhwCGQHz/k//D/3aAG4CDwa4AwcA+wHr+pz8uf9xAUoEPgRH/QH+cvro/l0FAQMxBtkC2/8aALgBBvyjAAj+MAXiB4UCEwVI/oj+pP62AnsBqAYZB1MIDAGuANcAaQW/CsoNZgndBlkIrgqDCagGmQJYBGEHSQrTBusDLwLdA1AHLwYmA18BXQBNA7cD/wKPBpMDrAUhAcwEXgLaBGoE9wMoA9kBHwnFA1EIPQV2BHUBsgDXAU4EEAH0/97+W/tN/14AuwC8AHX8Ffww/9X83/upAQT8BgWcA6kCnwBH+lL5xQHSAzIDsABg+U78LvwuALsBNvl6+M/8sP+OAbUC7/mV9xP53PZk/sf6MPtg/FT6lv2p/OP4KvZZ+VP+IQIzART9Y/c496b6CAD6+zT77fjQ+ej8DP4e/iz7V/nB+jD/CwKZAxH+Hf5H+Cf6k/xj/s7+EP5nATD/JAJu/ez/KPpd+JX9E/7/AHABzgG1AVwC6//PA0EBOgHMAgcDAQAi/tf9+P03Ac3/cANeACr/qv8Q/UX83PwZ/x8EfgKrBMT/TP2S/E4CeAbdBjcIBgAPBAQChgqDCBkHGwN1BLkCmQJ7A2f/fAG5AqYHtwM+Asr7bPlO/TABpAilA2oBwv1X/o4BZAXKBJD+hQDl/k0DIQZSCDYHEgOZ/tIAMAGFAd8EsQSMAiv9G/sv+xn+HwWyB6sEi/xo+sD62P8lBboGlwAT//H5Z/48BYcHmQPp+xb26/dUALQIRAtEBYj8+PnC+K39TASDBtMEyP7S+m37jfzSA0AIdwQj/Sv1CPJy+c8AzQauABX1bPDw8y3/CAn3CFX7U/MI8f/11wCWBFgCXPdj8ln0X/jX/TsD2QGA/LX6NPwZAAwCkQZOBZ7/pvoM/IP9agFoBo8GxQFG/u/7hP5FAUQGDAkvBh0CFf4F/l/+ewUBBfsDNACW/KL+QwAiCHoJ3Qe7/778ZfpXAdkHgwmyBfH9i/mq+qoAgwXsCzYGbwCg/g3/ggKvBZAE5wAY/5gAyQOcA4gCygMAAz4ErQZkAsv/vP5SACcDqQQ3A5j96PrI/vgDAwl2BWQBiv5X/1AE3QbWBtACGwAt/Mv+xPyX/Ef8v/s3/gf/Iv6o/Dr+Z/0hBWYE3wAI/F75swCuB0ALdQb3/A31Tfm+AXwFLwMb+XPzhvUt/mUHoAjUAV39PPqV+5wABAJ2/377r/wU/ikAXQFDAo0AhQExA/v+rwD5/TcBcP/B/QX7oPoQ/ST+0QJ//CT7qvgQ/HIBxgQWBVP8D/oA+koA5QLcBFQCHPx9/aT+dQIsBBL+8vob+Tb5lQBSBAgAjACK/Ij+hgMCBB4CTv71+1QASgWqA50CJP7f/PADyQf2B+ECEPxH+ecAVQYYB7gAzfr6+Tb/1QJ9BBv+Avfv+HoB/wcRC7cGJ/0B/mgD3gjqC6cCa/ie85v5WwVTC20KZv8R8xrvEfcpAxIKfAq1/076Dvq+AfQI2gYJBKwAl/9aAEoEgwP//1T9Ff9lAIIExQTOBMQAyACbBZYF3AdcAmAAnf3m/f7+zQFxAygCvQX9BZ8EKQU3Ad7/KAAXACIBy/7d/Wn9rv0gASb/rP7X+u36qPe9+Pj5VftBAST+gf9x/S79o/27/G/9UvvR/Ef8gP9EAF3/Ffuj97b3Bvkg/Bv7xPg19Cj00Pvv/5v/2/3V+Jv2jvxqAd4BeAO/AAr+ff+v/sr/Af4J/gIA9v7rASv+Lv/8/Sz/h/2U/34BK/8MAjgBxgKIAZYBPQIR/un94/7R/dr/XwBbAGoAOABaAJoBDP/D/3wB0P+FAWECZf/sAZcCjACFAIgA+P77/pL/9P9XAsICZAMoA7kAygAs/9v/qwH4ARYFRAV1AOP9Vf9eAc4CKgUjBLYCiwSABCwC1QHhAKQDOQFOBHoFYwVHBr8EuAXSAYAAzwDVAaoDwgKPBOwBnQKRBJ0E3wXpBRkHZgWNBCEEvgO2BIgEpgMzAC39dAIlA0cD5gNd/t788/oL/R//hf/F/gUBgwFg/8oAaf+S/c0AB/7o/i77ovkJ+1z8BP6m/bD9rfyX/dH8aP1S/Rj8QP6O/DH9Hv0N+yH8I/r0/LT+Kf9GAFn/Hvxv/Mf9vP1V/sT5k/iy+iv5jfsd/sz+Xv1Z/8/6APvP+on7S/9r+7X7e/nP+Bb8EP3G/pn8Rfue+2j9ggAb/nv9q/l7+Mz4MPc/+pn5Y/u3/JH9X/1D/Ur+//4fAMgAewKDAtwAkv1kAQUCkwSXBdMBOgSSAr0FrAZGAkgAhv1z/a3+twE+Ak8DHgJ2/wABKAELBP0EPgNlAnIBuACsAswDxQE6A0kCqwFsAhQDiwTwBA0Dx//j/X7+fv/WAZMCVQMoAkYDAwM9As4BqgNtBI4FcwTdBO0EzgUzA94C5QLGA4kGbQb3AwYDdgPICecKaQvuB3EDVgUhBhcE6/8D/sT8YP9YA0wCJQPIAXkF4AWiAcgDDwBpAo8D3ADE/ob/mf72AOwDrQCo/g/9efvL/JL/MQKkAnH/7/3m+Uz7EP9NAm8EjAOL/S75ePk4/fICSASoAvb/8vnG+WP6Hv2aAKADWwSz/9/8w/qh+8EA1QIrAd37Kvhg+vj86AFSAir/yfop+SP72/pT+3/5Gvz2/B3/9/8A/cr87fr9+sj8tf5VAEoBlQBD/1D7Evzr/RQA3wDr/hb+6f3E/YL+Bf2k/Ij9Qv7+/xcB9AIABCME0gBf/6D9nP92A+r/A/8B/ND6PAF0AkEFXQQEAvD+7/9iANoCwANHAiACbv1w/Fj7UvzAAmAI0gq/Ct4F7wFl/5YALQITA5MCEQIUAUP/DQLaAj4DGAXAAuoFqgMAALkAagCW/s8CrAQABZkHbAhrCuoGlgKnAF3/JQCNAVT/cP1MAIcBpAQ5BqgC/QDO+9j72gBXAXEC4AAz+6L33/g2/cUBdQT9A1sCxP4J/Ab9Sf/xAHkBCACP/i79o/1lAMIAWwJyASb+H/yW+5j+tQBkAywDJwFz/qv9gwCnAnQFDQS0AdL+Vv6e/rf8uvl19mP0R/j3+W/7T/3y+wn8+PzF/hL/O/4V/J/5c/pr+5P9O/yB/Kz6BPsK/bgAsQQXBq4DpAEy+zb5/vm//Ff9vf5c/aP8V/1l/ccAXQBR/wX+Q/xx+bj8AABVAuUEiAOPAaD+uABVASgBIwAt/v38aPyCADYEjwT4ATD/b/1l/GIAxQIDB+gEOQG8/7//wAF+Bh8GKgI4/sL7Yfvo/SMCUwSwA8MDYQF4AUwDGQUTA8n/u/1M/Y0ATQbLCO4J/gZcA3IBtASmBzMKPwbF/4n6Sf1PBBAMBw2iCKf/9PwOAa0GUgiOBtn+Lvpm+Qz+fAKHAvv99fn69zX92QSqCmkKQwT0+oH2XvjJ/VgF+QfiAlP8aPkS/AwA4gLkAC399fg4+SX9YQF/Al0BK/2Y+GL5e/sQ/zEAZv7o+UP2z/f3/KwDAQetBH3+y/lB/KYCkAg6B9cBqPnF9fD58f/wBWsBEvlJ8FTxDvuzBSEIiv+V9L3vQ/clBLMNrApK/b/wJ/L1/ZEL6BDRBfH21vBl+J4FQQ9LDN7/YPaM+XsH2RPkEnYGj/Ya8wz+EwznEd4Jk/sE8Ff1iQNlDr4PewYL/Bn8twP8DbIQIAno/gz6XP7BCIwKCwVE/Kz4G/5dB3kJgwUf/uT3af4+CGAIIAPK9Sn15vovCNMLPAW195rwpvUGAb0I2gOX9B7v+/N7BOAM3gs0/GXzPvTzAOYJbAdJ9bnne+h39J8E2g3YAur03PGS+roKbQ+fBb73Qu2c9B0C9Q3bBkH91vFi9GAAiQm5Bnj7U/Rn+OwDNA9sDIMGxvwE/8sCyQm0B/UAJv3g/ZIBQQT7AAT+Mf8MBPQHzgeNAv8AKQG1BQQJegfkAPoAJAMCB4cEMP8e+un8SAEDB1cHvAFH/IsBPQdzCv4FGP84+/v+AASHBZX9o/P88hH9CAWjCvQBBfj883r6wQBzAvz5+fK29EP/zgTHA4v3ce658d/9EwicBer6vvD08GL7pQSDBrD69/Fh8F/55v81ASj5qvOO9LL/bwbdBsj+Vvp6/RUE0AopB7r+fPbz9/b++QRXBr3/iP70/zQGTAr3Bvz/tf0Z//0ETwifCL8BRwBjAWAF0gauBWsD8P88ANoFlQY/Biv/8gDdAtwInwljBrYB+P3xAuYHHQmqBb4AvQB8AHQFeQPIAWH8qf3xASAE9QFZ+8L4VfsxAOEB4P+b+8v5yP9TA3MBm/2X+QX8eQLdBckCUvuG9tv3tPvj+uz0ZvKT9oT/MwOnAfH4n/OQ92gCEgcJAx/7JvVa9in9wgEL+4DzXfIS+54ELwXU/Rr3//fK/0IJDwi//tv6uP6kBT8GWQKF+hb7tQBlCO8Hef+L+hj8MgM2CGQGJAHc/RgDYwmOCXcCUvvy/BMDkQqKCc0A8vr5/eUGvAq/BS76QPZg+6cGAgtwCE8AnwARB1oM4gaJ+jLyU/Vb/wYEnADU+rX6iQRoDAELR/4n9rv4TgHSA57/3/c39ev8FgfMBzj/e/n/+iIBDQaKBH7+A/me+EMAOwH5/N31rfqz/jsFPQTs/WL5HPgr/0v/efzu/GkBdgeLBZ//8vTQ++UCUQi0Ayz8rvfW/24LRwwXAMf2a/nxA/cIhgQj+mn4bgE2EJsRIgUr+Uz8mAa0C1YETfkg+cIDThDMEFYGdvu+/uMJugwkAzb4x/bw/2wI8Ael/vX4sQBbDCQR7QgF/eT15fz3AvoB9ftV+CX/lAbaCJT/aPX49ZoCOQ1gCLj7h/HE8jj8awSuA9b9cP53AgIDlPw/9qj3CQBMCDkF5PsD9AD25vueAegAF/9//0EB3P9F+ZPyCPfC/MwFfwT2/gj31Pfx/6oF1gNi/GH3ivaa/KD/EP2R+Xj5y/yi/LT5r/Yc+nkBlwsGDtAG6v3H+nf9yQLjAoAAffzG+l7+1wCL/3v+Fv0mAfwDIQB9+mT7mv6OBYMJLQb0/5v9IQHnBRIFAQO8/6P/2wI9BbADogOmAsEGyQpeC08JbgSKArYG5gkWCc8CPP+4/SUDpwhtCfMFnATmBRMGzgOFAcsAdwOJBmcGmwJf/1D9DQEQBe8IhAhfBekAWP5G//wCDwWWAxwAb/y5+/39Z//8/+f9L/29/kMCtQNRAi//Lf+yANwB1QBE/f74uPiw+dD6+fql+pX6Tfuw/MX9Vvxi/Pf7VPsZ+4/71Ppq+8D8yf9N/3H9D/6O+7b5kf06+475sPfP95P5fvaH+Tb+BALKAwoCGvyf+nT7aAEK/QP39vgl9kb9lQHoAKkDlP5e/6n9E/o2/z4GmgQ+BKH77PPM+ZEAohJ4DOwCa/Os72EB1AUKDboKZ/WJ9qr2BvoKB28APAb6Ba/7tgsFBhsJbhObA0sEl/ja7QH7C/vTCSwL3vZJ9Df2cPoPC54GJ/2W+Mf1FASAC+MCHQCg/QH7kQehCJ8AEgFz+AX9WASc/OMEPgJu+u//3ffh+qUGDAXCEGoJD/nb+o71NASJELsEWwTt9bLzUwQmBJQM1wt5BtUOVQWfAa0ARvdoAWIBhf5E/RH2zgBTAwAG1Aj5/GL9JQF4ArAEev1J/vL+pQPfCqcGawH0AEoDHAluBmcGKgTJ/ggEWP6zASAEHQKRCXD++Pte/jP6NAhQCtQEYAeP94D6yQPAATUOKwXu/VQDePmTBdAElfu9BID34Pl9ACT0egPM+in1PAE87Ib5YP1e72IIvfiS94oEY+8xA8YAIvU9CcD43fyfBojuNv4iAif8HQ/r+6b18fov7RADYQUm+AUAa+9489AEavwSBpL8EOxM+sT3wQEYDNP+GgDI/KH7EAp3CfEEdQB79BL2nAX6CBAOpAcJ8635kP3qBq0VSPqH8Gjty+5jF84Y4BBFB67lu/r3CygQWiHG/H7wRfag8MUa3RNSB8IFueFo+R0KkQheHIH4ce9v+tvxGhpGE+X7Uv5z4V36Wgx8BP0SDfQT8c4E3/2SGO0LD/z6Ahb0GwbfC7j+Lwr//F7+8we+/UoEKQFU+2IFrf5F/z8GCgTaBDUFH/xC+vj7z/pwAmEA3/w5Bjn9QPzN/DjzW/2A/uP+GgRi9fr6Ff8m/lwJ3wAs+9T94PihCGQNTQjYB8L8x/34BZoFPA5GBRf6wvvy98YD7g4eBWwEG/nD9XsEswClARcA7e+K/i4DiAHFC0Pzk++1+LTx6Q9XBif0vfgL5Hn9lhOVBesP3u6N5wsBj/rDFFAKzeui9/fqPgOGF4L7ZgDf7TftaRPKCO0PSwRe5Lv5Kf31BqAYFPbd9KDzX/HZFcINlgPA/Rrm5f1KCr8I7g0c9H7r3vcI/WAQBAnO92z2UPIz/zQN1gZk/S70JvcbA2QK1gMs+Wvxp/P5BOgIsQKd/t/zEP5wDMIGCQf59+nyRARsBGcMegiL9an9I/xE/TALIPq1/dX/QfiUBMD/wf/ABVv74QWkAPP8QgN6+x0AGwSPAj0JPP6FAn0Ehv7xBIj6xvaH+SH5PQw1CeYFGges9nb9JQRrAtUISvkD+vsCpQEVDRYJ/frv+7n7/wOZCFUA0/88Ap790QfbB3b+kAM9AaMC3glE/uUH9AfWAE8Q7gBw+EwBw/aMBfUFBfx9CKH7yvzECxT5PgLzBOf6GAk2/FH8Tghn+HcG0gY7964Bjfxl/ogIN//oBQUA9/gfA0oCuP+IAhn/ngC/ASkDkAAs/S76DQB2BaEDUwMR/qD1I/syAt4CkgLI9nXypfe+/qYMowji+zj5Xfjz//wFewAJ+gDzz/ZeABEA2QJP/RT+4ATT/8sCpvlo8qH/8/40CTkJ1PUm/lj5CfzoDIX4hQDcAU737wpk+mbxCvu/7MMDewpm98EBp/KB+FMQOgSoCtv/8Ov9/tj9ZwHxCTb4L/7VAGn81Qea+7P1mAFYAO8Jiwyi/zD+qvl3/dgKwgNNAF75OfYnAKAFTwyZBSoCUgDK/goDb/yA/DACtvy/BPYBxfn7Arz+tgOPB8b8AQI9/oT94gSWAmMFKQakBZQA6fyv+7v7tgBVCK4EyANk/d/2TgUmAI4FxAhk88cA1ABb/iEPQ/lo+ygHCvjKDS4FRvDZAeHzQf9UDZ/5tAe3ApP7GA74/h39rAVx/KoCWAa//E4G/gSOAZAGA/oO+Un9QPxAAeECIQHLBaEDcgK0ARj8nQDsAnQGpQT//pv8YP5C/8wGygBh+x785PYD/mIBHvpWArwD6QHjCjoAEvqYBDP7ighOCWz2yAbR+BD6oQi3+XkDWQn3/mAGUft19NoI+gC6DN8OPPj4/+L7MAA6B8cAswJTAlP7/gZW/qf74Pqc92MCHf3A/8n8GPB3/C4DC/+JBVj2oPND/3T/agmvCzX6TwP4AKf6IgLM9XDwZf6u+Jb/xgGO8fb/4wB6/AcDzfk08qT/7gGYA1YPgfxPBS8AD/LABOL3I/yc/y/8xP5ZAxkDoP/n/vj3A/4zA5n+0wJC/tD78ASFCH8E5wWsAqj5eAeaAFn9ZgYF89D+MgYP9E8M2vgw8jMDNPUwAGcJ7PGBATf/jvL6Cj3+b/knDfz6Zwa8DDz4uwa+/U34PwTd/c3/QgLO/Tj9MQNcAFQGhQMEAMD/3/y9CkADyAV/CA73Ngz6B2sEwQ77/Fv+KAdC/AUA5Aeo9i0DGwFA84wFUP5q/qkMo/4OAOERRfm8CzIJn/RwDTMApQFoDkv3o/z0AdL1PgQyApvx9v55+lT34grx+ln7gQUB86EE7gAA+r//av70958DsgJs+GkJnPoN/KkIFvPX/iMHm/FhBiUDAOqtCqX6C/dxFnHzmQDcDQn1DAv5DRfw/A93/0X7WBLA+Oj8JQbK9ZP+bwQP8ckD7QM3/m0QpAAf/9sFgvVYAeL+jfsHDAAH3gbTCtkAegINBOUDwwHAB4388fy/AHTz1f4f/zP1LwPk/mb3Cwaj+eT5Jwtw8tUCcAJm7RwOfvq3/bIQEO4LAk79TewoDdT3/PQpDL/uk/6vEDDqCgjr/IfmeAp195f2eRDE9NX76gty8uYGjP/l8lwFcQM7AQILPfom9gP/E/Yj/d/+2O9S+Tj7jPoNB6ADhPksAAkALfzFD5YEUvs6DiT4hgW+FZHw8AgB/77kPhi+95z0rx0f3kEF+g/P22MSyvrJ5Owg+/7r8O0ivOMz+DkakelGECgMiu0OCeD+cfY6Cd4CP/xh/yf+kvT1/AIIg/RvB+AAdu4ZDWT86v0MElD0zAdBCPDyABCU/VT3IxLt8rYDjAxB71MRRQHM/HIJ7/fKA2X3sgheBE/+jBYY+d8FZhiP8dgRWg+H668f9Pwq8tkiu+7iBKoiZ++4DmoToOPEEqkL4urjIqP/ROyJKCrmsP0JI0rdqBhDETbm7ia9+2T7WSYd6r8HoxDr5JsZQAMs73cVzeew9F0SguZSCM8MfehFDpkD/u0ZErL5GfdEESP7dP/bBtD1PvxrBsf7r/uDAJDqkPbs/cXzTQTY/5LxRQDT9wP4tQYu9832W/+M9HL9fQVj9bj7zAFY8WkGIAMd9dwFtfX18OUEaPIe9ssEqeec+yUDv+vuDKL5Lu34D7nw8QDRFfLuwQ7aAnDtcw6b+ML2tgjR7Kb6jf/57icJTvwd+qwGivV1BFYCw/vjA7T8JPzNAaECnfjy/yP+U/n6CYEAa/myAuH7HfcQDB/5ifWLCs3uVwBGE+3wAQLaC/XjnQdLBnfkDRaY/RHzJyE6+372wB4q8HgEMCCh6MgIhgQu4foONQfT9SUXnfea7A0UMvaGA9MiQvIiAtYQWN9kD0gUYe7ELNsBv+rfI0Xfkf7YIxnlrRrNDQXgsBbM+W35CC42+BAAGQsk2gAMbhai/5olXvtO6q8LIfU4EacqyAHVCeD8SebJBxcLnQcIFej+nPAf/7b6GQhvGd0BI/wr+l7nogCLC+IDlQnt/dPwQv2uAD4CUg4cBET3f/bj9IH1zQZfCK4ANwN/9PPz+ADuBnwDQQky9vXuzgKy9cAKzQs18Jz7dfSq738KHwPM9jgJ3uts9McM2vNfDFgHC+zKAGn5s/CaEaT9+vTfB4LnkvlCDwf50QmUBYro9f6HAKv2kxDu97PyEweG9pAHvQxn8qr50Puy9gkQPgvr93b6G+3h8RUIMgQiAcT30e5F6mAANwXr/j4PK/Gt7AAMaPleD6EfqvBDAiUDxeaoGSwHoPGSFmblNu/NE7vpCA79EMHlVwuV9sXoFBlm+DQDMRb06zQC/waZ81MYcAkt+lYMje869scSpPQ6C98HxuHcBiT8U/TZHF/0zfbTBh/rGQb8FO//zA3yCIb1xApkC28ImxBnCA/5rgK9BZr9dRac/lD74A+v86AJahFJ79oKrQSJ8Hsd9fz47TwaQ+YZBM8kbe1tExcM1eeYIPUMFf5YId/vvPNsFvXulA37Ed7h6ASZ/7LwcRzgATn1BwqT8dP9TRnu/4f/1ga88Mz+uhTh/FcF/QYG58QEfwuA9g4T0fkN5lcGyu+DACQau/DFAjT+gOeJDkgDjfg8D1Xv2vDMD5j1SAWYDmTnwwDMBj7zPBWG/K7off1d7OT2jhZw9z/0ugNB3p4FyhS17+4S5ffc3goQSfN7+DIc/ub/8y0HqN4/C5MRyukDFGn1iuevFrzr7wAAEWjjAAuyAGDloxDt7W337hX97iv+cAM95CIK5Ag88lsMbu2S7lkOKPsDDrAAEO/r+aX3nQ4GEdH/0gF66vHy6g12+woGFv7F3CQDAAN8/Kwcr/Po7DwIwOleDaUVqfGPDmD4au7FHkb+sQn7FUDhxgF4ByvykSO/+Tfslwck4cUEaBZ+7YURSP7Z68Yh9/w5/30lo+jcCtsUA+QbFykF9OuZGqH1WfU5F7rzAgrqFUjtyQqvARHtNxSL/h/6/xVw9xT9ehA49e4IYRed90sKYAo89CoNIQ8M+CsEqQFu8/4LRg71+qAJ0QFW+MMQNAOX+3sGs/h4/b4HxvnQ/SABQ/aECDoKGPn7CPr9ovVTGh//qgFBE7TjMwO9Eb7rBBl3AJnjWR1G87f9uSmm2F4JURGD1A4oFfut2rovJeLC9gIwj9OOB24cW9IkIIEKkt/GIonvQ+x+IK7hDwSRE1PZHw+8BLzfXhs8+O3rpRXP7pX07hCe6k77fAdg9H0FNgMa+h/8v/uHAnH9hwKV+kD4GQVU+/wG+wYw5LwJYPl/7WEmR+8N/GkgxN8yE8MQLOPbIPXwPO8jGErYWQ8PAIvkwCHi4Q/6Kh2C1HENoAD31tAcrPj77RcX5uF9/4EIB/kKGkH0DwSDBZzqeBaK+Yzx9Q9I79v/KA0Z7Wf+7vj+85MPLvuLA8ACmeb3Bfr/svFnCMD7Te2xEOYGXuyTF670cudKKHDuxv9FIqPg9A2mCk3kXx/5+Lv1Yhyk5fwFRhKc53kUuQae72UTuv6K7m0NuP1F8zYZKP1w60IcsPSQ8ZQtw9/PALghec8KKewId9mGOr7bavXqNXDCTh8AD4LIbToL6a3pbTgdy0MRVRQD1iwoRvad7SUdxuQXBqUQ9OvKDh8GW/YqEdgDwgYNCtMAvgUC/gkDDgjM9q8OXQqq9uUVlgDo8UgWUfhzAUsYGuehD4MB8+AfLbDiGvgoO6nK7BvFLWTAzTu4CRHKQ0ek23vliz5rxdgWJRcizocvwe/S7fYv4NRBEYIap9h1Ip/+0OJPHzbxC/0yChP0efs//NUGXPj7/ZgL8OerD6sL/+p9HLzwb/GhGCfdCg9CBf3fTSg07QPwOSb40C0HJRg72YUQ/wx224cKCQtv3+UXOwem3zMbCu6O6LIjTeBuAhIS28+zHGj+SeNGK2nhEffQGqrZSQ83Btzr0A539HXzjfmC99AGsPVTCIfz9OW6El/XuQzqDbvPMCvV1ODqLTFVuJsqxgXcwDxGk8PM9tY7WbHJNxIEE8gvSGfH8QSYIGfC7SwR7ZrkJCXOzZgQa/1c44wm3+QWDH0S1d0+JE/rV/c5Ii/cthIYDCzbvxeY/3nnHx+/+5/qxSTc96XzpSzN7mr6Wi+l170RXR3yzgQzX/+G4rFEtdZ5DCswLdCSKzIGOdumMgHtdfyhLZXk5AwwGPfmwxfjByHuUxVJ+9/32BYA+tEGKQ/k+wYH5gbzB7z/XAtyBrfqrxoj8uT5GCtm18YZ7w2L0qQ35uIs9rM5XMhUIjMLXMnqMiDh9vU/K4LRWgp5/y3kmBGg+3IBMv2GArH4wPwlE97shBGjB5DinRWL7LDy4hYB6u4I4wXM8FME0f9o/dD/ARAd+Gj/PAsD4FAVTAUV6awqUd1G+v4Xs8/MJLv/r+HpKiHSPwDXDRDSISRE8Avv4hwj0gYROQJD6WMqFeQ6/kYJsdYaGaT73fakHUnmU/mTBWDs5gt9DAPyNgFP/RXoBAxzBa3uOxLX40frgA0v5tYT9Akd5dQYquED+qYbHOQbGq75uuW7DgLkuQ/ICd3yYx5x3B4KQAUF5jAyQeqhBdYXV80/JQ723u8UO9fSkhWUDSDU7TSz6XX6LyOFz9QaSwFz6+Iv+ORfC7EGgeTxF/z1EwpiEjzuMhQN7kYE9gvk7h0Vd/Pp9kcT6O8wGEcMg/e+GazvNv6FEczx2wuSCenooAmPAcry/Rh//pbzVhdu6ekKkSIt6/YhYvnU4JcdCOKpDQAen9i6IHzqWOqGKk7fZB0vDRbcFiwT4VYJ6ife2G4dWfDK3gomqOY2GI4RleK6GAbyywfbF/r3sAhD8c0CxAJhCMYXqetODVvxxe5RJbng2xDF+vbRoSFm444Jix4f0P4R2ui57L4sF+kTFLEAj9mHFgXrWws3Ey/h0Ac777HtZR0I8x4Ikgfe4j8IGgRr9JcYt/iN8ZkMlORoAAMG0+WfD57yM/DJEJjpXQXoCdrrNQyx92PxOAQ19iH/jweL+/L23/pK85z2aBAM+0oB5whz4tIJWAfy8iAgx/Cj7fsP/eSWENsQh+v3DYjrYPSkEG36shUTAK3yKAZR76oV2wqt+NAQzeLyAEATy/W5Heb4tecHClbptgwTDcbzQQj16nEEaAqQACgXmO68/7kIDPXfIST+G/cMA17nTwLnEVkDWwca+w/zbPcIDSL+P/99CQrgcwxdDNvy3Cr98SHycBvB4VQV+A0B5eQTtu7F+pgYE/PGDnn4nfH3C1nykBIBAHT4WBNB7VMWkwZj8Foh2+AuDa4S/+McK/rssf39IGPazyKP+LTrvidG2CgZngim3ecrFtzCA6cjLtplIgn/WuPHJErlnAevEIvq7RAd/RAAMQ4u/cwJCfcvATIDbvZKEa7w0fmuBz7rkg64//f0bQav8Xb/YAEnBGQDDfajA4rqBgiuC0zvgBm/5ynulxbw46cXFQCj43sPAuUCFU4VaPO1FOXVlP6ECXDyFi7J4A7xSQYX1lAx2QDX7TIbf81jDDIbVvCZJZ7hqOblDHvtGCLfAXTwygD83ooQTwSlAhILutobAzj85/qSJS3s/ftbARPsLBvzCTkBhQZ/7roEyAPsBDIEne8Q/aTweAGDDzno/Q3r8xjm3CI15kEBnRlZ1vUikvxF4rcoNNYO/p4ajNdKHuz8SeX7HYvuF/1XEmPunwJSCBH38ANoBzL0CAeLBJX01wyJ+SD7OwfA9N0BJv32BTgG5gPuDFH0Ig0uCZ36cBjf6yf9tAO+8lwYLgKgBO3/efjnBn/9OxJI+4P6ygMe7hAU3QMw+nsOBu/gC1QQb/lwFDvygAHvDXH4Lhlm9VYFQguC8I4iPfQ6An4cJduEHpD/G+A4L4PdB/+DJ/7MiiGC+lPahDIx49AOlR2c32caivnIAWgUCvhDDNPyKwLbAhbxUxh66fUCTQ9z4LAeh/it8DcireMHDicQtep2Em/4FvirC3z7ff4EAtj7tvqyCcf8WAFCCQjyYAyt+7/1/Qym7X0Ltv5i8RoLe+kpB0gEzfVcE4rraAUIBoXrshgW7Oj8sQ9Y3xESDvTg6S8dHOXwCGkQReH0EGbul+enFOrqfQEACJfnnANw/lr7rAmx/pz6FgIFBW/9DAwS/QD0jgxN8dELnguK7LoTOPDw/qMVwOp5Eoj3we7TFeLu/guuBvzk1wrt8uj7Shn16qwDsAKB5Psd//lK8GMcWt2oDIIVjOMTIJTuDeo3Hz7l9AwODWXnihRj/bT75AwR9qj/3QZGBHQH/ARp/Qn93AKg/un+YvuZ+MEAZAGVBl8DEv0iA2b/6wDFA6b71vrxAfz5rv5+B2ruGgb9ABvpihhu84X4QBzk26AJ6gYM5eMf9PKk9X0TNuuwClAKSPhvCL/+Fv73/wQFSPYc+yYFb+36DVUAwfIaHBDyKgxuFfjozBZK/CH5NRiL6xgHAwDQ+aMX/vhvBw3/8fb1EeX9mgY7AeH2rwj9+2MDBf4s+LsJmgDnCq7/PPmFCZD7gg6LBLrzPAZc8MsCqgrY6ZELNvkD+AUe+OkOB4IGFON5INf0KvD5G0XYKxIqDw/ooiSS7Nz35han68sUsgOq+FcKqfkdA4j+DgJN/kT8Awt79sEFg//36twKtfll/pYUD/FkARsFPeydDeb/+fGsCkbwJv07C9vuqAeH+3r1cgxi+coH5QD9+jUHX/ywDb8CcgbZCb/sxg3K8yr0vxU93bYK9Qbh5T8qevLN9/oiS9+4FiEOLt9UHtPqFvTXJgfnvA6tCGvkbxfh97QDKgtE73IIVfN5AOL/NPLODoz1ng0YDmj0ZREW9+b93giV8RcAW/yJ9KwBg//1+VEMPAWyAroRJfQNAHsFCfbWE0/zlfp1AEbqPRT/+XH8kAj84jsNYf+o+q0Xje4gCRgJk+6BEJv1EvsbEJHzngj3AZHzfQe5+xIDvAfe+54DA/gi+h/6zPj2CZv3Qw/5Bwnzsxt67If/thQi3nQdr/ci5mwfEtkRCywWjuZDIffvmfAlFi/kQAnhAfnnAQ/E9HwAlAp67f4Gl/8d/mQW1vfmBO4Ec/C3EsD/8vuPDu3vWgksCN70BQwr9Pb4Wwfb/jcLU//G/0z7xP5IDlb6bQni+lnuGhLq89ELEw9q51ARvffd9hoXwuk5BaH/bOmiD6/2TfdrBd7x8AECB2n9+QKR/b/x9fzt/Fb3PgU8+Wf4rP6R8xwAS/4cACMCov6CBVD23QI9+o7y3hF47xkL1g7G4v8UKfCD7mcgSuR1D/gLN+YZHsL3wfvyE/3wHAbJAqf7EANxA2QAb/whB2b08PxMCWbzsg5m/FjsqgWW6+4BuQ3X90kL7vyf/F8MzwJxCZcB7vyH/o363ARx+X8B1QHw9SIPuPz2AHIOjO1IC9v+NPJ3FHfu6gWgD43ucxbI9hf1JxXC7pIWyQ3K8jwWIfAUApYSsvHKC8D5p+92BuX3o/5MA873wfuqB8cCogJcBr7uh/VP/iPyigl//xXy1gPO80r9Swsb/KcE5f1K9KkAcP6PAtAIGwQ6BHEEWgMs/Dv+p/1894wMygH4/p8NRelPAwQMn/PDJZf9Q/XBGLnkdBYJFZvviyCW6prx9Rb+6owSFwzy8BERe/tu/4kHTvgz/un9JwPOAvgFdgS++EwEHwGiANAOSAN9AvMFTfaq/38FN/79CPQC/fT4AHr8yP0DETwChP4MBCfrDgCPCS/6LBeL/DTuyQPS5sYFaxAc9zURz+5U9LAMy/DyGEz99OsbC5rdlQutEwXvRB+S8+jxUB1o6/cM7QjV4sIOoPUG9UwSofQsAnMM9PsmBfwHTfQL/p0FVe8cC1UE9OyMD07uIPUDEk/oJA9jBa3n9A/17Aj/sxge88kNnACy7cUNbwLaBzMN4PUg9a348v31C7QP4gQT+Mb1jvdBAPALsgWp9gX5svBg9jQQWgL2A50CROoaBK0ARP5MD8XwwfhiBWP5zBN9BcD39ACw78j8Zgn8APgGn/1U98j9AwG1AysEhAQpAFUBbwbG/CsCqAHf+UUPtAcyBj8OMfI//q4CmPriEJf+Lvg3//zxUQk4Bgv/uwQI8k/9YQXiAMUNtPvz9qf+nfTeBN8B0/m8AWv6/QNYBMkDWQER90MBFvqB/4YFG/Q9Ab35//cwCHH+9QAJAQj41/wp/0QC3ABcAvj+XPmWBNr67fxsBzD1Dgi5Cef4aA7L+x73bQvX91IHuwjk828F2fpN+awHfv0jArn+v/2sBBoGTwqQ/RX+W/mX9okMT/00BvUHse8mDRz+NvrDEhXsEP6JATvvgxJGA9v7sAl38okCrghK+j0FzvZ38g0Clv57BeoHPP5BAO4DdwI/CM0Hh/7+/ur9O/oTAuf+uvWy/Wn6GP85DpcCGgflBJ31wwKl/yf8WQZz/ZgA+gAL/uwCUP3T+yX7iP0tAUb+qAJW+yb5EP98/NQG3wNlAhEF5/ruB2j/of7NBGjyuACH/Yb2IwY08+H8eAWd91kKFAEJ+2oImvi0AQ4C7PciBeD8pgD6Auv6UAGy/LkATwPS/B4AHPpH/cQC6/1nBij/1P0CB34AmQYRBGf+LQOe/lYDIwQP/Pr/Gvvv/bkG/P9cCesA3vxJCVT2IQKxAhzwkwpU+GX79Q5Y8l0JBQa19eQR4wDEAmgQCPe2AYgG6fcmB5wAD/MyAtn6dPpyCoH5Xv7xAvLyFwbY/+H75Anq+q4EBAYAAu8I1ALnBSUDPwPQA1j+mwVM+dL/+P9l9ugGo/zaAUQHPvifB4b8AP18Brn3GgaRBO0AuQpl/0oDfwIiADkHTADMBlP+Bf5PBSL3KQi3/Kz3ggYd8SIB1QCY8K4GPPkz/oESpvZWB2IGbu8ODsn8bfoGDQby3/7FAT/3eAXa/WX/e/2R+6X/DfmMAZ76M/rXASvxRQHK/vv5ogxi+hUECAky+fIJmP8d/cED6/go/an4lPU1+Kn9SQG3/RYFqv0z/wQIbAGnDWMF4/4yAi32lv2k+Uj4H/759kEA7gHzAfMGL/8uBF3+Ov0LAnn21AD6/zf6YAYL+TP5mAOH97YDpAdK/BMKmAGy/7cJzftR/5wCPfnLAvICcvxgBGwC9v9aB1gDofxXBjL8o/4YClv4Ywaw+pnvTAW08zMC0gl19GQMv/5l/WMPaPgfA+8HCforCcT/9voJCOz71QMXCOz3OQn2/k//bg0W+nEHnwKx9mgH/ve3+t4DmflrBy0HUv6gB5T+MQACC4AEEAPMBA7+xABqAwf/pAMxAtMBHgrqBLAFDgVj+9YAbgE3/esC6/pl+Dj+NP0V/qEFAgNzACsFKvsxAdgCSvv7COz+PPztBVP4zP9aAtr6gAOIAa39UwBT/Tz4Yv2Y/Qz8PwcZAOj7agXd+QICvwT19ycEW/uQ+rQGBPY3APgBjfl7BaD4R/aa+KL1uf+i+03+7/o49p4Bi/qP/lEEw/yOA3EA7f2O/6H7RAH7/8P+VQBe+yL/cv8bAvwBAP2cAOj6zf9jBEb7ZgWL/yP5ZAZR9xr70Adm+IkM6Qk0/rsQBf08AFoRRPm8CUkJv/XpCev/sfvKCkv8VgaBC2oCegn4BOj+3QIWAAP/6/7v+1b9UP/oAVYDhwN9Akj9NADV/t3/GgSSAVgDtQDTAFwBKf7nAbr8G/ycAH/70gNKAm358AOc+mP8wgWS9n0AewKr/EIJ2P09/M8BufmhAD//Qvli/Uv6A/3s/97/Rf6V/Xz/I/o+/Mv7Sfg4AEX8/P1bAJT4p/22/jb9hwKj/2L+FwK0APr/xwSWAY7/9gL6+s78gQAT+7UEVAKP/hsKFQK1ATYEd/pNBQwGOgG4A0z70fmQ/5EBDv/MAfwAs/6gCe0DGQNnCFX78wDiASf65gPd/gH9tgMm/AIAgwH4/CEDhQL/AWgCVgDs/kn/DQPH/LT8pfyr86YB+wAW+10FP/QG+EH/PvQDBjL/n/1JCJH1CAC8AYb6aAvX/YT6PAE2+N0FJwey/cj/0fZ593T+hf17/3sCEf0i/Nb+jPiCAREHbf/3AzT+G/aiBL4F1gKVCDv6evcF/eH81AnjCcAJoAUi/Pn/iv4UAowHhQFpAdn7gfvcAaoCnAl7/gn5Fvxd+Y8JqQtHBDcG0fkkAC8GXwF7DWoEB/4TBCX6KwOvB0sE+wdV/Cj3gfjK+g4GQAkhBJL6vveT+/H+rAkWBUD8Z/yV9pj9ggWpBC4IHgG2+Pn7IP4uBnoJYQG7+uPzmPW5AAEGfQPq/ov2aPGN+XP9EwFXCmf+cfm392bycAO9Bk0GdQkt9zb67fzd+QcJJgDd/y0CafaMA88BMANcCO/2I/lx+cn57QVEA4IAdPz08yX5V/ll/yEGagQhBiUAngJrAb8DBwwvAUsD3f1U9UwIlQOPBPIDSe4H+JX4KALDEs4BJwIv+NHyXwaFBtgM7wWz8s7zEfnKBjsQoAyLAEfw4PN1/BMJTRA+CGMAwPNv+PQEhw9jFucDnvn86xDwOQfSB7YPUwOP9ND6LvuBDXMS/goQBin1Z/b1/2oH0A65A9r4H/F787ECDQuNDsQGc/di9/b8uQQzDQYGwgDt+tX1xwL5A+4HVQtB+t/42/PD9zoGLwQjBor8WvXa+T374QmTC50D+gCH9u36rAStCPgKyvsS73zu1PKQBP8PMwoaAAzyZfOI/kEM7hctCOr6zvP98okIAA3aCU/7vug77/H2iAjlDHj+Ave07IH0SQdqCToL1ffy7Iz16PpdDDIMj/so9gPwePhhCPoFeAPs+UXzxftSAaYKAQdL+y/6TvevAU0FRP8P/vLwDfUtAC0DCQxt/zr75P1d+WINBwniAXUCnvFu/egC3/+9B375evl5+xD6IwnFA8j+rv6H9b38/ANyBxEJOAF6/SD8bgEuCB8G+ARw/lX7MABSBWcKVQb8/rX91Pn+AAsIEQHq/sf7FP3JBwsHNAj5AQL7pf+pADAKQQw6A0MEZPti/s4EjQS9ClL9WfnQ/+T8dQmOBhACdv/j9hMJEQoABA4H3/XY+nwD9QZ1DdD7HvfU+jj/KhLGCywBy/kW9WsG5g14CwAChO5W9B3/1AqCD377cPLU8in/vBM0EMECiPR47i7+1wvADLAE7fTU72r58weKDN0E2/os9Sf4FQgXDXgGp/+s9E78PgXqB8oLj/fp8K30J/RRBwICa/bF+Bnvm/y0AUb7x/7W8pj3LAedBFII+gEo9Oj1xfurAFAE9PsZ85rzg/XQ//wDCv6A/Rv6w/40B3cC4/7k9z75SQELAlIF9vox9X75Ef6bCawFfv8r/cr3eP95AHIBt/8q+UkETAWoBDMGjPlU/uv//gEVCOv/zQCn/1j/hQXK//kCzAZYBKQLiwOMAQQB/vllBVYFYQH1Amn+OwMbBXgFZg1BBEACTAldANUBDAFH+y0BsABxBTQG3QEcA1D/DAW4BSwB+wQKAfQBwQPf/4UBE/od/CMGnwYqCkcExP7q/a/8fQOnBZUBX/3A/ZsCwgCWBf8C/Pl+/+UATwN2BOb9z/5uAJMDXAd7BDIAnvvn/Q0C6QEuAPD9Sv6N/qkCZwKs/+n98vrz/twDrADF/8H+jvwG/fcAvwQGAAr+1fmG+SX92fx6/9T5TPU4+bb4Qv2v/RD1dvp9+m/7HQSm/Tb5Gfmf9dr8kf/D/EUCqvnB/pcBGv+UBIf7Mfpl/lH+WwT+BG3+//xI+3IBVQPY/PD4KPGd8n3+TgDlAFL/cfjQ+938Ivt4+3X2sv6fA5oBogMT+J73Kf48BMQI3ACK+cT3KvoSBMIDEwHe/xX7KwREBR4AxgGG+uL/ugR7Aez/wPwo+RT+ZQBCAy0EQwA4AwcDUgEcBSkGRwOPBK0Ajf95Aa4CpwhSAisFDwE7/HEDHAQ/AqgKOAj1BA8EDQPIBHwEBQz8BRoCmQBV/hcDcgPDAhYC7/9yBeQHSwcWBv/7GANKCGQKMA4MAwj87fyAA8UGVAJ3/X/3dP7jCmkIeggg/8P4HAPvAjIGnPw4+hH+tf8GBqgCffzI/tL/NwM8BG/+nQIyAOgGxApTA2kGQQEv/8AAdP21+wwACAAeBssCLgCQ++H3Gfqt+c/6TPzY+Lb85fvb+BL9ZP3CAeACKAGH+yD4CPsEAI4E2/4T+tr2D/hc+BAEAPpw/PT8D/kFAxD85/iV9Yv2r/FF/Vj2qPYl+MTwlv18+ZoHlgE5BDAFRAA6A4cIc/21AWMAuP2cBuX8mwM+/6v8GQfqAHoJbwTEAQwBQPwF/i0BcQLDA2D/avplAbD49wgXA0IEHQO3/AL/EvsY+vv7CQF2AYIKHwLuANT5af7P/pUF5gZqAjcAKf91/y3/5QZxAC8F4/6l/YT9Df9PACoBggYmAcsImQjkB+8IjAQDA2QC0f/jA/H9aAIHBbYA2wcCAVkAkAKY/BQIdAW7BhQHp/4B/Fz9jPokBn8AlQR8/uz7FQEK/A8HwwZvBtX/RPzn9ur+I/5tBncE9vzc/n329/5N+H39GP5M+nMCcwEu++8BT/v6/mAH5gCABU39xvp3AD/8CgfV/ib+xPxi9538Rv3b/kcCwgFKA1wDsgIoAZv99/0k/UT8Iv7Q+y7/4PyVAwUETQTzBQn/oQHH/AwA1ALM/2QDmf7//6IDGwKkBFL/0vwo/i78WQMl/4n/g/+c/iAE6ANuA+oDu/3SAqQCzgBPAv36nf2T/r3/8AMNAlv+vgFy/V3+4gGt/cMFPgH0BYMBU/0Q/977pADqAyMExwMGADL8HP6W/J0DcwTWA1QDe/1C/zH/CACsAG4AB//n/MH8a/0i/YX85//0/e/+sAD++gL+6/qr/1T/cf6tAPH9vwAGAdD/j/op+l/3f/xD/fv/xf0v/D37EvjT+qz5tPgB+Y37hvq1/0MB5/5hAqb/CgOmA7ICHwL4/On/YQAVAwQIPgKCAAYBD/zcAukBlgLwAkP//wMDAnYCewNJ/fgAmgIqA9cGawJD/Rj8SvykA/8Fgge3Av77j/zF/R4AcAMBArX+if5///v+PP9O/g3+hQBJAx8FGARo/yf+HP4w/9H+jPwx+Wn4ific/S0BawFlA9//b/4h/+r7h/99/nz/DgL0/aMC5v1T/M4BpP7rA1IEiQApAlz8If5UAfoAzASi/qz84Pk+92EAaALTAVIBP/xD/Mz+uwCpA1oAnwGnAX0CaAbOBhcG5QfjByAHOAWY/93+iP2XAPgDfQRhBPQBEQIUAhMD4wQoBHIB6ABH/5H/hQHoAc8BegFdAt4FGQhWCOgGwAGXAicDjQKBBVgBRP2S/Yr9Y/7C/tP+xPoi+fP9Z/4KAS0BXPyt+qX5rfzk/jz/3v2V+Vn6+Ps5/0oBYAAp/437Zv1y/D37UP0e+tn6hv0X/mEBRABUAewBhQCTAywAQf2f+o/4e/zzASoFxAFd/rP7hPxbA+0GkgRX/Mj4kvmz/nYHRAjjAQX88vBk7075v/fh/xP7G/MY++j+1QrnDx0IxP+C89L3RAJ5BVoKzv+n+M32Sv2iCmwK3ggcAKT3DfzyAQ0LQg+qBVT+Ifpq+78JXxAVEnoNEf8M/Wr/WwcqEpgPkAjF/0L34/kzAdgGywvGCGMDHv6T/kf/wAGFBUEBRAHX/oz/SAAF/o38o/y1ACYC4wXrAo/64v03/v4AZQf5/Rf6m/ZK9SYCnQZ+CTkGZ/3c/PL7IwEUBZr/uPxu+WL8kgLyBooI2P8g/F/7QvudBuYH0AKJ/w77sf3PArsH4QRj/IP2UvZP+h3+w/3j+Bf3sfim/hsC9f8t+sr3APlC/LICRAemA9YB6v6n/f8Byf/lAlD+svl/+3n5qf7U/Fb5efos9RH55/xN+SH7dvd1+Hb9z/7IAX/9Pfti/BL+UgBNAVD+g/oE9zn2V/vR/CUEgwPO/ykB2/41A74EqwL0/xz7kfmF+w3/mwF9/9z+Zf3K/KwCqwHYAr8BUvwX//z+ywBgBMcCnwKqALf/zgFaAToDeQKOAFYBQQJaBq0GhAbYAjcA7//4AS4DvQAUARH6af3FAk4BkQXoAhv94f89/igDvQOk/pP9G/dq+Uj9U/9nAlz8x/0Q/4UAUgnEBlUF3wLc/e0CmwLKBeIFrABUAvD+IQGWBTMCJQOT/9b9ngBDASYEqAHp/9sBQANVCEoKMgY2Aj0BhgL+BWUHIAhFBL0CsQMEBlMJVwhNBuoB2//hANAEmQSxAj3+Rfu9/8YBOwg4Btr/JQDH/SYD+AV8AaACifyB/WoBfv7YA7L+YP1bAaH89gHJABYAvQO7ALwAoQDe/Vz+6vxMAFn/Sv/z/wf79ftP+hL7dfw9+0f9M/o/+rL6zPcp+T73zPgk+0z5tvzY+T340vrg+wr/5f7//LX7cPsb/5kC9gIJAp//AwBtA1YFjQTRABv9EfpO/tz/7v/+/j35v/nl+gv/CQQLAaH93PuM+SD/DgMaBVQFdv3D/27/eQJrBosCSwG9/tT8UQJ2Al3/w/uY9/r5U/vQ//oAWf1y/B38JP05/+D++fud++H+LAFuBKQC4PyD/Lv8WgVHCGMI/gNS/68AwgMyBL0ExP3W+Jf59vv5AcEAtv9z/2r/8AbWB48HjgCf/Ej9Mf51AwAD2PvK+Gb13fuSCDUJiAr/AUD7Cf9RAV4KJgZQAXX/OfqNAdcDUf5+/+T3G/tHAOz/HwZwATwCAwZYA5UJgQUyAXYCHP9BAgUEnALwAjL8Tf2b/+wAywVQBVcBlACQ/ET/SgA2ADUChACR/4UCgQObCOMIlQVcA7r+DAEQBW4GcgW0+4/2HPb/+5wFLwVY/wf5+/SU/bMF1wlQBBz7HPxT//YHugtBAvz7Afja/zIMyAx9Cc7+hPaB/V4DsgmjBJr9RP0f/2QHcAyNBOL+Mfj9+A8AOQLhBO0A0P+BAv0CWwM1/vD2vfR39hb9WgLd/jH7SvJG8kT2RfzQ/yX+5foA+wv+SQR6AxgCpP23+Uf+0gDS/qH7Vvc7+J78hwDcAhP8P/ge+TP5Mv6u/h38OPza/G8AogKTAfsBh/1I/fX+gPua/pP+//3VAMv/WwD7AfgBiAJs/2j+nP1A/xACBgagBTABbQBB/Z3/tgOTAw8FVgFG/uAALf7aAeT/2vz7/5T+bwKWA74BfgNoAfkE1wb+A1ICy/4/AeAFgwg0CBUDWv26/WD+2QH3AFj+FPoT/g0FfAbnCBQD0v03/qv+aQBJAPH7Av+2/oIC3APB/+T93vmd/Pj/gP9XAZkAIAA6BLIDJAP8/9H5wvu4+uD+gQHT/uECigKbBbgKlQVpBUYAIf8wBCkEjAaKBcr/WAGGATsEyQWbArQBaP6gANoC2QK1BIQBqACdABL/Sv+//J37Tf9+AkYGfQThAcn9VPuc/Nb+zgHjAMkBgQRLAtUCQQCy+x/8SfxT//j/pf7K/tf8n/8zAjYCfQMiAB3/5P6R/S0BDAFwA4QFcgPvAhwBKf9I/sX9Tv3n/Cj4k/gI+Mv5Z/xz/sr+Wf5Z/bL+0//AArUEzASQBVwC1gJcAWf/Avyt+uD8qvxhAecCYv5I/J74xPeS+gL8Nf9+/Z396f5h/Yn/Rf3Z/Aj9GfxS/hv+6f2E/+T/9v1z/+r9cf8+AFL+xf6Z+8z7Mf2o/uMAZQBt/0gAnP/4AOr/FP4x/of8SP57AV0BoQBOAGP9mvwF/Wn9L/40ArMCfAQEBkcFTgQXBDwCuQJkASECsgElAhYCnAFkAIMAPgE8//QBIgH8AMD/o/8FAKf+yv8uAo//fgKVAHkA0QJ6/34ATAAr/bkB3P/IALgBnv8NAW0CTgKBAaH+u/3g+nj9Sf6z/SsAkv9iAVYETQOgA5QAjf94/lP+uQFGAYoBZwKSAeMCmAHuADUAv/+PARYEkgQsBcQDKAQOAokDewPBAq8BHADEADH/kv+2/x7+1/6j/wsA0AGsApQChQPRASwBAwNzAuQC7gMwAdT/0/4j/q/+Cv6iANj+Mv/NACv/dAL6AeQAMANy/kgAnAEsAJMCJwIkAhoCrAFa/6f9Jv2P+4L8Vf2R/Br9Y/rE92D4wfOU8/H0QPjB+1/+UgA9/Qz8Zfvo/ET/WP2B/av+UP8+AkYETgLh/qb8L/s3+w3/2//sAEgCBAHGBIgFrAahBwME0gC2/cH8K/7bAeEBmwQOAff/PP5v/jX/RP/4AD4BF/+F/uj6fvv5+v75v/4n/Hf9lwDX/v4BQQRWBhsD3Qf4BNICYQMBAvoA/QHBAt4EfwLoA///bANlBKkDCAbrAqr+gQC9/W/+vf0j/Az9jfjR+v37Kf/4BdQFKgcGBQsB1AET/xUCGwFPAkD/HP9+/TX+nv0J/+f9Jf7u/jH7swDa/dr8HwCC/FgAXQJ6ABkEkgGoAy0EIgXyCNwDrwb+BR8BUgTD/uQA4gGU/igD4AAhAsgCQwMiBh4ECgVNBLMAgwE2Ad4AlQLiAqwC4AJ0AX8B8P/bAHgATgJIArICowA9/wn8pfqS+vf6hfsQ/XL6OvyX+mL7u/6l+87+hPqa+bb5APTE9wj4Zvln/pr+KgXu/Qz87/ay8Kz18vggAZ0JOwikDJoF9QEaA33+tv5U/B/7M/yb/HgCkv9i/3H/gPnP+jn4efjV+gz5gvwq/AoAEgGnA9MFUAinB+0M8ASKCO//ef/n+0r6o/14/oEC/QIaAQkE9vxFAgwCzQFuB10FngZ3CFEFEQcXAs0DcwTJ/6wDJgAb/koAev1HALT8w/2D/eD6q/6h/TP/Nv6x/Oz6gPkk+gb93/9OAfoDpgGiA2IAiADw/r/8Dvyc/NT7z/0G/SD+Kfzj/CH/lv17AfUDUwHPBCADYwINBV0BOAa+A/MErwbBBO8HkQQZBgsGUAQBBkcDUQMuAx8Ayv4F/5z8tv9/ALYA8v/RAPX9pf7l/lMAHwAxAOsA1gAyAq8BSgEcAvb9Tf4U/6v7zf5nAWAA9QKHAVkA4P5m/gX/ef2D/0r/E/57AWQADAMXAuwDagNcArsCSgF3/2kB/f0=\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import namedtuple\n",
    "from functools import partial\n",
    "import gc\n",
    "from pathlib import Path\n",
    "\n",
    "from diffusers import AudioLDM2Pipeline\n",
    "from IPython.display import Audio\n",
    "import numpy as np\n",
    "import openvino as ov\n",
    "import torch\n",
    "\n",
    "MODEL_ID = \"cvssp/audioldm2\"\n",
    "pipe = AudioLDM2Pipeline.from_pretrained(MODEL_ID)\n",
    "\n",
    "prompt = \"birds singing in the forest\"\n",
    "negative_prompt = \"Low quality\"\n",
    "audio = pipe(\n",
    "    prompt,\n",
    "    negative_prompt=negative_prompt,\n",
    "    num_inference_steps=150,\n",
    "    audio_length_in_s=3.0,\n",
    ").audios[0]\n",
    "\n",
    "sampling_rate = 16000\n",
    "Audio(audio, rate=sampling_rate)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert models to OpenVINO Intermediate representation (IR) format \n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "\n",
    "[Model conversion API](https://docs.openvino.ai/2024/openvino-workflow/model-preparation.html) enables direct conversion of PyTorch models backing the pipeline. We need to provide a model object, input data for model tracing to `ov.convert_model` function to obtain OpenVINO `ov.Model` object instance. Model can be saved on disk for next deployment using `ov.save_model` function.\n",
    "\n",
    "The pipeline consists of seven important parts:\n",
    "\n",
    "* T5 and CLAP Text Encoders for creation condition to generate an sound from a text prompt.\n",
    "* Projection model to merge outputs from the two text encoders.\n",
    "* GPT-2 language model to generate a sequence of hidden-states conditioned on the projected outputs from the two text encoders.\n",
    "* Vocoder to convert the mel-spectrogram latents to the final audio waveform.\n",
    "* Unet for step-by-step denoising latent image representation.\n",
    "* Autoencoder (VAE) for decoding latent space to image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "models_base_folder = Path(\"models\")\n",
    "\n",
    "\n",
    "def cleanup_torchscript_cache():\n",
    "    \"\"\"\n",
    "    Helper for removing cached model representation\n",
    "    \"\"\"\n",
    "    torch._C._jit_clear_class_registry()\n",
    "    torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()\n",
    "    torch.jit._state._clear_class_state()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CLAP Text Encoder Conversion\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "First frozen text-encoder. AudioLDM2 uses the joint audio-text embedding model\n",
    "[CLAP](https://huggingface.co/docs/transformers/model_doc/clap#transformers.CLAPTextModelWithProjection),\n",
    "specifically the [`laion/clap-htsat-unfused`](https://huggingface.co/laion/clap-htsat-unfused) variant. The\n",
    "text branch is used to encode the text prompt to a prompt embedding. The full audio-text model is used to\n",
    "rank generated waveforms against the text prompt by computing similarity scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`loss_type=None` was set in the config but it is unrecognised.Using the default loss: `ForCausalLMLoss`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text Encoder successfully converted to IR\n"
     ]
    }
   ],
   "source": [
    "class ClapEncoderWrapper(torch.nn.Module):\n",
    "    def __init__(self, encoder):\n",
    "        super().__init__()\n",
    "        encoder.eval()\n",
    "        self.encoder = encoder\n",
    "\n",
    "    def forward(self, input_ids, attention_mask):\n",
    "        return self.encoder.get_text_features(input_ids, attention_mask)\n",
    "\n",
    "\n",
    "clap_text_encoder_ir_path = models_base_folder / \"clap_text_encoder.xml\"\n",
    "\n",
    "if not clap_text_encoder_ir_path.exists():\n",
    "    with torch.no_grad():\n",
    "        ov_model = ov.convert_model(\n",
    "            ClapEncoderWrapper(pipe.text_encoder),  # model instance\n",
    "            example_input={\n",
    "                \"input_ids\": torch.ones((1, 512), dtype=torch.long),\n",
    "                \"attention_mask\": torch.ones((1, 512), dtype=torch.long),\n",
    "            },  # inputs for model tracing\n",
    "        )\n",
    "    ov.save_model(ov_model, clap_text_encoder_ir_path)\n",
    "    del ov_model\n",
    "    cleanup_torchscript_cache()\n",
    "    gc.collect()\n",
    "    print(\"Text Encoder successfully converted to IR\")\n",
    "else:\n",
    "    print(f\"Text Encoder will be loaded from {clap_text_encoder_ir_path}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### T5 Text Encoder Conversion\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "As second frozen text-encoder, AudioLDM2 uses the [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the\n",
    "            [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) variant.\n",
    "\n",
    "The text-encoder is responsible for transforming the input prompt, for example, \"birds singing in the forest\" into an embedding space that can be understood by the U-Net. It is usually a simple transformer-based encoder that maps a sequence of input tokens to a sequence of latent text embeddings.\n",
    "\n",
    "The input of the text encoder is tensor `input_ids`, which contains indexes of tokens from text processed by the tokenizer and padded to the maximum length accepted by the model. Model outputs are two tensors: `last_hidden_state` - hidden state from the last MultiHeadAttention layer in the model and `pooler_out` - pooled output for whole model hidden states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text Encoder successfully converted to IR\n"
     ]
    }
   ],
   "source": [
    "t5_text_encoder_ir_path = models_base_folder / \"t5_text_encoder.xml\"\n",
    "\n",
    "if not t5_text_encoder_ir_path.exists():\n",
    "    pipe.text_encoder_2.eval()\n",
    "    with torch.no_grad():\n",
    "        ov_model = ov.convert_model(\n",
    "            pipe.text_encoder_2,  # model instance\n",
    "            example_input=torch.ones((1, 7), dtype=torch.long),  # inputs for model tracing\n",
    "        )\n",
    "    ov.save_model(ov_model, t5_text_encoder_ir_path)\n",
    "    del ov_model\n",
    "    cleanup_torchscript_cache()\n",
    "    gc.collect()\n",
    "    print(\"Text Encoder successfully converted to IR\")\n",
    "else:\n",
    "    print(f\"Text Encoder will be loaded from {t5_text_encoder_ir_path}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Projection model conversion\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "A trained model used to linearly project the hidden-states from the first and second text encoder models and insert learned Start Of Sequence and End Of Sequence token embeddings. The projected hidden-states from the two text encoders are concatenated to give the input to the language model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Projection Model successfully converted to IR\n"
     ]
    }
   ],
   "source": [
    "projection_model_ir_path = models_base_folder / \"projection_model.xml\"\n",
    "\n",
    "projection_model_inputs = {\n",
    "    \"hidden_states\": torch.randn((1, 1, 512), dtype=torch.float32),\n",
    "    \"hidden_states_1\": torch.randn((1, 7, 1024), dtype=torch.float32),\n",
    "    \"attention_mask\": torch.ones((1, 1), dtype=torch.int64),\n",
    "    \"attention_mask_1\": torch.ones((1, 7), dtype=torch.int64),\n",
    "}\n",
    "\n",
    "if not projection_model_ir_path.exists():\n",
    "    pipe.projection_model.eval()\n",
    "    with torch.no_grad():\n",
    "        ov_model = ov.convert_model(\n",
    "            pipe.projection_model,  # model instance\n",
    "            example_input=projection_model_inputs,  # inputs for model tracing\n",
    "        )\n",
    "    ov.save_model(ov_model, projection_model_ir_path)\n",
    "    del ov_model\n",
    "    cleanup_torchscript_cache()\n",
    "    gc.collect()\n",
    "    print(\"The Projection Model successfully converted to IR\")\n",
    "else:\n",
    "    print(f\"The Projection Model will be loaded from {projection_model_ir_path}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GPT-2 conversion\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "[GPT-2](https://huggingface.co/gpt2) is an auto-regressive language model used to generate a sequence of hidden-states conditioned on the projected outputs from the two text encoders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ea/work/my_optimum_intel/optimum_env_new/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:116: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  if (input_shape[-1] > 1 or self.sliding_window is not None) and self.is_causal:\n",
      "/home/ea/work/my_optimum_intel/optimum_env_new/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:164: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  if past_key_values_length > 0:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Projection Model successfully converted to IR\n"
     ]
    }
   ],
   "source": [
    "language_model_ir_path = models_base_folder / \"language_model.xml\"\n",
    "\n",
    "language_model_inputs = {\n",
    "    \"inputs_embeds\": torch.randn((1, 12, 768), dtype=torch.float32),\n",
    "    \"attention_mask\": torch.ones((1, 12), dtype=torch.int64),\n",
    "}\n",
    "\n",
    "if not language_model_ir_path.exists():\n",
    "    pipe.language_model.config.torchscript = True\n",
    "    pipe.language_model.eval()\n",
    "    pipe.language_model.__call__ = partial(\n",
    "        pipe.language_model.__call__,\n",
    "        kwargs={\"past_key_values\": None, \"use_cache\": False, \"return_dict\": False},\n",
    "    )\n",
    "    with torch.no_grad():\n",
    "        ov_model = ov.convert_model(\n",
    "            pipe.language_model,  # model instance\n",
    "            example_input=language_model_inputs,  # inputs for model tracing\n",
    "        )\n",
    "\n",
    "    ov_model.inputs[0].get_node().set_partial_shape(ov.PartialShape([1, -1]))\n",
    "    ov_model.inputs[0].get_node().set_element_type(ov.Type.i64)\n",
    "    ov_model.inputs[1].get_node().set_partial_shape(ov.PartialShape([1, -1, 768]))\n",
    "    ov_model.inputs[1].get_node().set_element_type(ov.Type.f32)\n",
    "\n",
    "    ov_model.validate_nodes_and_infer_types()\n",
    "\n",
    "    ov.save_model(ov_model, language_model_ir_path)\n",
    "    del ov_model\n",
    "    cleanup_torchscript_cache()\n",
    "    gc.collect()\n",
    "    print(\"The Projection Model successfully converted to IR\")\n",
    "else:\n",
    "    print(f\"The Projection Model will be loaded from {language_model_ir_path}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vocoder conversion\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "[`SpeechT5 HiFi-GAN Vocoder`](https://huggingface.co/microsoft/speecht5_hifigan) is used to convert the mel-spectrogram latents to the final audio waveform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocoder_ir_path = models_base_folder / \"vocoder.xml\"\n",
    "\n",
    "if not vocoder_ir_path.exists():\n",
    "    pipe.vocoder.eval()\n",
    "    with torch.no_grad():\n",
    "        ov_model = ov.convert_model(\n",
    "            pipe.vocoder,  # model instance\n",
    "            example_input=torch.ones((1, 700, 64), dtype=torch.float32),  # inputs for model tracing\n",
    "        )\n",
    "    ov.save_model(ov_model, vocoder_ir_path)\n",
    "    del ov_model\n",
    "    cleanup_torchscript_cache()\n",
    "    gc.collect()\n",
    "    print(\"The Vocoder successfully converted to IR\")\n",
    "else:\n",
    "    print(f\"The Vocoder will be loaded from {vocoder_ir_path}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UNet conversion \n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "\n",
    "The UNet model is used to denoise the encoded audio latents. The process of UNet model conversion remains the same, like for original Stable Diffusion model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unet_ir_path = models_base_folder / \"unet.xml\"\n",
    "\n",
    "pipe.unet.eval()\n",
    "unet_inputs = {\n",
    "    \"sample\": torch.randn((2, 8, 75, 16), dtype=torch.float32),\n",
    "    \"timestep\": torch.tensor(1, dtype=torch.int64),\n",
    "    \"encoder_hidden_states\": torch.randn((2, 8, 768), dtype=torch.float32),\n",
    "    \"encoder_hidden_states_1\": torch.randn((2, 7, 1024), dtype=torch.float32),\n",
    "    \"encoder_attention_mask_1\": torch.ones((2, 7), dtype=torch.int64),\n",
    "}\n",
    "\n",
    "if not unet_ir_path.exists():\n",
    "    with torch.no_grad():\n",
    "        ov_model = ov.convert_model(pipe.unet, example_input=unet_inputs)\n",
    "\n",
    "    ov_model.inputs[0].get_node().set_partial_shape(ov.PartialShape((2, 8, -1, 16)))\n",
    "    ov_model.inputs[2].get_node().set_partial_shape(ov.PartialShape((2, 8, 768)))\n",
    "    ov_model.inputs[3].get_node().set_partial_shape(ov.PartialShape((2, -1, 1024)))\n",
    "    ov_model.inputs[4].get_node().set_partial_shape(ov.PartialShape((2, -1)))\n",
    "    ov_model.validate_nodes_and_infer_types()\n",
    "\n",
    "    ov.save_model(ov_model, unet_ir_path)\n",
    "\n",
    "    del ov_model\n",
    "    cleanup_torchscript_cache()\n",
    "    gc.collect()\n",
    "    print(\"Unet successfully converted to IR\")\n",
    "else:\n",
    "    print(f\"Unet will be loaded from {unet_ir_path}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VAE Decoder conversion \n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "\n",
    "The VAE model has two parts, an encoder, and a decoder. The encoder is used to convert the image into a low-dimensional latent representation, which will serve as the input to the U-Net model. The decoder, conversely, transforms the latent representation back into an image.\n",
    "\n",
    "During latent diffusion training, the encoder is used to get the latent representations (latents) of the images for the forward diffusion process, which applies more and more noise at each step. During inference, the denoised latents generated by the reverse diffusion process are converted back into images using the VAE decoder. During inference, we will see that we **only need the VAE decoder**. You can find instructions on how to convert the encoder part in a stable diffusion [notebook](../stable-diffusion-text-to-image/stable-diffusion-text-to-image.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vae_ir_path = models_base_folder / \"vae.xml\"\n",
    "\n",
    "\n",
    "class VAEDecoderWrapper(torch.nn.Module):\n",
    "    def __init__(self, vae):\n",
    "        super().__init__()\n",
    "        vae.eval()\n",
    "        self.vae = vae\n",
    "\n",
    "    def forward(self, latents):\n",
    "        return self.vae.decode(latents)\n",
    "\n",
    "\n",
    "if not vae_ir_path.exists():\n",
    "    vae_decoder = VAEDecoderWrapper(pipe.vae)\n",
    "    latents = torch.zeros((1, 8, 175, 16))\n",
    "\n",
    "    vae_decoder.eval()\n",
    "    with torch.no_grad():\n",
    "        ov_model = ov.convert_model(vae_decoder, example_input=latents)\n",
    "        ov.save_model(ov_model, vae_ir_path)\n",
    "    del ov_model\n",
    "    cleanup_torchscript_cache()\n",
    "    gc.collect()\n",
    "    print(\"VAE decoder successfully converted to IR\")\n",
    "else:\n",
    "    print(f\"VAE decoder will be loaded from {vae_ir_path}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select inference device for AudioLDM2 pipeline \n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "\n",
    "select device from dropdown list for running inference using OpenVINO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from notebook_utils import device_widget\n",
    "\n",
    "core = ov.Core()\n",
    "\n",
    "device = device_widget()\n",
    "device"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adapt OpenVINO models to the original pipeline\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "Here we create wrapper classes for all three OpenVINO models that we want to embed in the original inference pipeline.\n",
    "Here are some of the things to consider when adapting an OV model:\n",
    " - Make sure that parameters passed by the original pipeline are forwarded to the compiled OV model properly; sometimes the OV model uses only a portion of the input arguments and some are ignored, sometimes you need to convert the argument to another data type or unwrap some data structures such as tuples or dictionaries.\n",
    " - Do guarantee that the wrapper class returns results to the pipeline in an expected format. In the example below you can see how we pack OV model outputs into special named tuples to adapt them for the pipeline.\n",
    " - Pay attention to the model method used in the original pipeline for calling the model - it may be not the `forward` method! Refer to the `OVClapEncoderWrapper` to see how we wrap OV model inference into the `get_text_features` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OVClapEncoderWrapper:\n",
    "    def __init__(self, encoder_ir, config):\n",
    "        self.encoder = core.compile_model(encoder_ir, device.value)\n",
    "        self.config = config\n",
    "\n",
    "    def get_text_features(self, input_ids, attention_mask, **_):\n",
    "        last_hidden_state = self.encoder([input_ids, attention_mask])[0]\n",
    "        return torch.from_numpy(last_hidden_state)\n",
    "\n",
    "\n",
    "class OVT5EncoderWrapper:\n",
    "    def __init__(self, encoder_ir, config):\n",
    "        self.encoder = core.compile_model(encoder_ir, device.value)\n",
    "        self.config = config\n",
    "        self.dtype = torch.float32\n",
    "\n",
    "    def __call__(self, input_ids, **_):\n",
    "        last_hidden_state = self.encoder(input_ids)[0]\n",
    "        return torch.from_numpy(last_hidden_state)[None, ...]\n",
    "\n",
    "\n",
    "class OVVocoderWrapper:\n",
    "    def __init__(self, vocoder_ir, config):\n",
    "        self.vocoder = core.compile_model(vocoder_ir, device.value)\n",
    "        self.config = config\n",
    "\n",
    "    def __call__(self, mel_spectrogram, **_):\n",
    "        waveform = self.vocoder(mel_spectrogram)[0]\n",
    "        return torch.from_numpy(waveform)\n",
    "\n",
    "\n",
    "class OVProjectionModelWrapper:\n",
    "    def __init__(self, proj_model_ir, config):\n",
    "        self.proj_model = core.compile_model(proj_model_ir, device.value)\n",
    "        self.config = config\n",
    "        self.output_type = namedtuple(\"ProjectionOutput\", [\"hidden_states\", \"attention_mask\"])\n",
    "\n",
    "    def __call__(self, hidden_states, hidden_states_1, attention_mask, attention_mask_1, **_):\n",
    "        output = self.proj_model(\n",
    "            {\n",
    "                \"hidden_states\": hidden_states,\n",
    "                \"hidden_states_1\": hidden_states_1,\n",
    "                \"attention_mask\": attention_mask,\n",
    "                \"attention_mask_1\": attention_mask_1,\n",
    "            }\n",
    "        )\n",
    "        return self.output_type(torch.from_numpy(output[0]), torch.from_numpy(output[1]))\n",
    "\n",
    "\n",
    "class OVUnetWrapper:\n",
    "    def __init__(self, unet_ir, config):\n",
    "        self.unet = core.compile_model(unet_ir, device.value)\n",
    "        self.config = config\n",
    "\n",
    "    def __call__(self, sample, timestep, encoder_hidden_states, encoder_hidden_states_1, encoder_attention_mask_1, **_):\n",
    "        output = self.unet(\n",
    "            {\n",
    "                \"sample\": sample,\n",
    "                \"timestep\": timestep,\n",
    "                \"encoder_hidden_states\": encoder_hidden_states,\n",
    "                \"encoder_hidden_states_1\": encoder_hidden_states_1,\n",
    "                \"encoder_attention_mask_1\": encoder_attention_mask_1,\n",
    "            }\n",
    "        )\n",
    "        return (torch.from_numpy(output[0]),)\n",
    "\n",
    "\n",
    "class OVVaeDecoderWrapper:\n",
    "    def __init__(self, vae_ir, config):\n",
    "        self.vae = core.compile_model(vae_ir, device.value)\n",
    "        self.config = config\n",
    "        self.output_type = namedtuple(\"VaeOutput\", [\"sample\"])\n",
    "\n",
    "    def decode(self, latents, **_):\n",
    "        last_hidden_state = self.vae(latents)[0]\n",
    "        return self.output_type(torch.from_numpy(last_hidden_state))\n",
    "\n",
    "\n",
    "def generate_language_model(gpt_2: ov.CompiledModel, inputs_embeds: torch.Tensor, attention_mask: torch.Tensor, max_new_tokens: int = 8, **_) -> torch.Tensor:\n",
    "    \"\"\"\n",
    "    Generates a sequence of hidden-states from the language model, conditioned on the embedding inputs.\n",
    "    \"\"\"\n",
    "    if not max_new_tokens:\n",
    "        max_new_tokens = 8\n",
    "    inputs_embeds = inputs_embeds.cpu().numpy()\n",
    "    attention_mask = attention_mask.cpu().numpy()\n",
    "    for _ in range(max_new_tokens):\n",
    "        # forward pass to get next hidden states\n",
    "        output = gpt_2({\"inputs_embeds\": inputs_embeds, \"attention_mask\": attention_mask})\n",
    "\n",
    "        next_hidden_states = output[0]\n",
    "\n",
    "        # Update the model input\n",
    "        inputs_embeds = np.concatenate([inputs_embeds, next_hidden_states[:, -1:, :]], axis=1)\n",
    "        attention_mask = np.concatenate([attention_mask, np.ones((attention_mask.shape[0], 1))], axis=1)\n",
    "    return torch.from_numpy(inputs_embeds[:, -max_new_tokens:, :])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we initialize the wrapper objects and load them to the HF pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = AudioLDM2Pipeline.from_pretrained(MODEL_ID)\n",
    "pipe.config.torchscript = True\n",
    "pipe.config.return_dict = False\n",
    "\n",
    "np.random.seed(0)\n",
    "torch.manual_seed(0)\n",
    "\n",
    "pipe.text_encoder = OVClapEncoderWrapper(clap_text_encoder_ir_path, pipe.text_encoder.config)\n",
    "pipe.text_encoder_2 = OVT5EncoderWrapper(t5_text_encoder_ir_path, pipe.text_encoder_2.config)\n",
    "pipe.projection_model = OVProjectionModelWrapper(projection_model_ir_path, pipe.projection_model.config)\n",
    "pipe.vocoder = OVVocoderWrapper(vocoder_ir_path, pipe.vocoder.config)\n",
    "pipe.unet = OVUnetWrapper(unet_ir_path, pipe.unet.config)\n",
    "pipe.vae = OVVaeDecoderWrapper(vae_ir_path, pipe.vae.config)\n",
    "\n",
    "pipe.generate_language_model = partial(generate_language_model, core.compile_model(language_model_ir_path, device.value))\n",
    "\n",
    "gc.collect()\n",
    "\n",
    "prompt = \"birds singing in the forest\"\n",
    "negative_prompt = \"Low quality\"\n",
    "audio = pipe(\n",
    "    prompt,\n",
    "    negative_prompt=negative_prompt,\n",
    "    num_inference_steps=150,\n",
    "    audio_length_in_s=3.0,\n",
    ").audios[0]\n",
    "\n",
    "sampling_rate = 16000\n",
    "Audio(audio, rate=sampling_rate)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try out the converted pipeline\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "Now, we are ready to start generation. For improving the generation process, we also introduce an opportunity to provide a `negative prompt`. Technically, positive prompt steers the diffusion toward the output associated with it, while negative prompt steers the diffusion away from it.\n",
    "The demo app below is created using [Gradio package](https://www.gradio.app/docs/interface)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gradio as gr\n",
    "\n",
    "\n",
    "def _generate(\n",
    "    prompt,\n",
    "    negative_prompt,\n",
    "    audio_length_in_s,\n",
    "    num_inference_steps,\n",
    "    _=gr.Progress(track_tqdm=True),\n",
    "):\n",
    "    \"\"\"Gradio backing function.\"\"\"\n",
    "    audio_values = pipe(\n",
    "        prompt,\n",
    "        negative_prompt=negative_prompt,\n",
    "        num_inference_steps=num_inference_steps,\n",
    "        audio_length_in_s=audio_length_in_s,\n",
    "    )\n",
    "    waveform = audio_values[0].squeeze() * 2**15\n",
    "    return (sampling_rate, waveform.astype(np.int16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "if not Path(\"gradio_helper.py\").exists():\n",
    "    r = requests.get(url=\"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/sound-generation-audioldm2/gradio_helper.py\")\n",
    "    open(\"gradio_helper.py\", \"w\").write(r.text)\n",
    "\n",
    "from gradio_helper import make_demo\n",
    "\n",
    "demo = make_demo(fn=_generate)\n",
    "\n",
    "try:\n",
    "    demo.queue().launch(debug=True)\n",
    "except Exception:\n",
    "    demo.queue().launch(share=True, debug=True)\n",
    "# If you are launching remotely, specify server_name and server_port\n",
    "# EXAMPLE: `demo.launch(server_name=\"your server name\", server_port=\"server port in int\")`\n",
    "# To learn more please refer to the Gradio docs: https://gradio.app/docs/"
   ]
  }
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